MCP

Generative UI... in Python? — Jeremiah Lowin, Prefect

Sep 10, 2026 · 17:38

Jeremiah Lowin, founder and CEO of Prefect and author of FastMCP, explains how MCP apps — a protocol extension introduced in January that lets tool results bypass the agent and reach users as HTML, CSS, and JavaScript — led him to build Prefab, a Python DSL for shipping UIs from MCP servers. Because FastMCP's users are mostly Python engineers in enterprises sharing tables, forms, and charts, he refused to pretend to ship React from Python and instead scoped the problem to composing pre-built components via nested context managers and reactive variables. Lowin argues the JSON serialization in the middle is the real point, since a serializable UI can be generated, received, or edited by an agent. His team also found the Python representation is about 70% smaller than JSON, so they now stream Python over the wire and convert it in a sandbox, and the Prefab docs are rendered entirely in Prefab with live-editable examples.

ACP: The Universal Remote Control for AI Agents — Alex Hancock, Block

Sep 9, 2026 · 11:01

Alex Hancock, a Block engineer who works on the Goose harness and the MCP Rust SDK, argues agentic AI has a standard for agents reaching outward (MCP) but none for clients telling harnesses what to do — in the worst case one client per harness, like needing a different browser per website. His answer is ACP, the agent client protocol from Zed and JetBrains: JSON-RPC-based, carrying sessions, user messages, tool calls, and permission requests, and extensible via underscore-prefixed custom methods so shared patterns can move onto a standards track. He demos Zed and a Poolside terminal client driving the same Goose agent, plus a new HTTP and websocket remote transport. With remote transports for protocol, MCP, and models, all four pieces of the stack become independently placeable.

Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked

Sep 9, 2026 · 14:09

Brandon Waselnuk of Unblocked argues that AI-generated code should feel like it was written by someone who has been on your team for years, and that the gap is no longer intelligence but context. He traces how bad context compounds as teams move from tab completion to parallel and background agents, producing correction doom loops, wasted search tokens, and a review tax. Two common fixes stall: the curated context trap, where markdown files rot and someone must curate them for everyone, and the MCP plateau, where an agent may never call the server or stops at the first plausible answer due to satisfaction of search bias, missing last night's Slack correction. A context engine must resolve conflicts between an old architecture diagram and a fresh CTO Slack thread, personalize relevance, enforce permissions, and deliver token-optimized context. Running the same prompt with and without context cut tokens from 21 million to 10.8 million and finished about two hours sooner. He closes by demoing three open source tools, including a workshop that builds a relational context engine from scratch.

500 Skills, Zero Fine-Tuning: LinkedIn's Playbook for AI Agents — Ajay Prakash, LinkedIn

Sep 9, 2026 · 20:25

Ajay Prakash, senior staff software engineer at LinkedIn, explains how Contextual Agent Playbooks make coding agents reliable at enterprise scale, turning an on-call alert into a mitigated incident in minutes. Early vibe coding failed: agents trained on open source hallucinated across LinkedIn's 1,000 internal repos. An internal MCP server with code search, docs, and Jira fell short: tribal knowledge sat in stale wikis, context overloaded, and sessions started from scratch. Playbooks, instructions published as tools, work when self-contained and split into small referenced pieces, and agents open PRs to refresh stale ones. Since MCP degrades past 30 or 40 tools, everything surfaces behind three meta tools: search, get schema, execute, now 1,300 tools, 600 playbooks, 8,000 daily users.

The End of the Static Screen: Architecting Intent-Driven UX — Gus Iwanaga, commercetools

Sep 1, 2026 · 23:19

Gus Iwanaga, product engineer at commercetools, explains why static UIs are giving way to agentic interfaces that assemble themselves at runtime. He walks through how an LLM orchestrator can take a user's natural-language goal, query structured product data, and compose the right components on the fly — a pattern his team now uses instead of hand-coding every screen. The talk lays out the new stack this requires: schema-driven design systems that keep AI output consistent, guardrails and human review to hold quality in place, and intent classified by model rather than clicks. Iwanaga argues that teams who keep designing fixed screens will fall behind, because the future of enterprise software is interfaces generated at request time, adapted to each user and each question they ask.

x402 isn’t good (yet) — Jan Curn, Apify

Sep 1, 2026 · 20:48

Jan Curn, founder and CEO of Apify, argues that x402, Coinbase's HTTP 402-based agentic payments standard, is promising but still has rough edges. Drawing on Apify's launch of 20,000 tools on x402 alongside Coinbase, he explains how the protocol works: a client signs a payment, a facilitator verifies it, and only then does the server settle on-chain. He details the double-spending window between verification and settlement, the conflict between x402's mandatory 402 response and MCP's 401, and why metered billing pushed Apify to charge fixed amounts and refund the remainder. He also explains why crypto, unlike credit cards, suits micropayments and one-way agent transactions where buyers cannot dispute payments.

Multimodal Collaborative Agents for Next-Gen Commerce — Nidhi Kaushik Vyas, Google DeepMind

Sep 1, 2026 · 21:08

Nidhi Kaushik Vyas of Google DeepMind argues shopping agents fail as wrappers around the search bar, assuming well-formed intent, when users arrive with only a vibe — closing the articulation gap is the agent's job. Her discovery-research-response loop starts by building a working state from conversation, context and reference images, separating hard constraints from soft ones an image implies, and flagging inventory as a real-time variable. Research picks the highest information-gain question — room width, since all is moot if furniture won't fit — using visual boards for subjective tastes. Response adapts format — summary for policy questions, comparison tables, imagery — and autoraters grade every stage, including counterfactual tests flipping query parts to check constraints move when they should. A Q&A covers merchant ontology and UCP.

Tribal Dungeons of Global Shipping: AI Agents at Global Scale — Dmitry Buykin, Maersk

Aug 29, 2026 · 12:02

Dmitry Buykin of Maersk explains that global shipping's hardest AI problem is not the agent loop but the refining loop around it, turning tribal dungeons of screenshot-based SOPs into executable procedures. He reports 200 production instances at scales up to 10-minute latencies, with a procedure corpus 20 times larger than runtime because the same step differs across countries. Accuracy was earned through over 100,000 corrections in nine months, heat maps turning traces into priorities, and corrections counting only when they become executable changes. He advocates five moves — make work representable, execution bounded, behavior observable, correction cheap, improvement compound — and skipping MCP for tuned composite tools, since 'please be careful' is not a guardrail.

Agents Are Where Microservices Were in 2015 — Roberto Milev & Uday Kanagala, Navan

Aug 29, 2026 · 19:28

Roberto Milev, chief architect at Navan, and Uday Kanagala argue agents are where microservices were in 2015: master a single agentic loop before multi-agent orchestration. At Navan, agentic workflows run on AWS AgentCore with custom session persistence, composing context from skills as pluggable units. Traditional logs fail when agents emit too much thinking, so Navan uses pre/post-tool hooks to emit goals, reasoning, belief status, and confidence scores into BrainTrust traces, routing inferred answers to humans. Testing is nondeterministic, scored by trajectory evals; cost, replay, and standards remain unsolved. Guardrails run before and after every tool call because 'book a flight whenever it's under $200' blurs who authorized the purchase.

AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

Aug 28, 2026 · 20:31

Imad Touil of QuantumBlack argues AI-native organizations run on skills, where know-how lives, and ungoverned skills become technical debt. Most engineers create skills but few share or maintain them, so workflows are nondeterministic; hooks fire on events, subagents protect context windows, and teams rarely write MCPs. Using microservices design, he prescribes reusable, modular, discoverable, composable skills, citing Anthropic's standard and a skills bench where deterministic skills beat models alone. His centralized platform—searchable catalog, MCP/CLI access, dependencies, versioning, access control, evaluation—counters duplication, decay, ownership gaps, and insecure skills; governance falls to architecture, infra, and cyber leads; a 15-team simulation shows convergence once governed.

From AI-Assisted to AI-Native: Building a Frontier Development Team — Clare Liguori, AWS

Aug 28, 2026 · 20:57

Clare Liguori, Senior Principal Engineer at AWS, says Amazon's frontier development teams achieved step-function productivity gains by deliberately changing how they work with Kiro, its agentic coding assistant. In a 50-team pilot, half improved under 3x in deployment velocity and the other half hit a median 4.5x; the difference was habits, not tools. She defines frontier developers as writing 1-2% of their code, letting agents run for hours, and running multiple agents in parallel. The five habits: invest in agent context, slow down to speed up by refactoring brownfield codebases, feed agents specifications instead of babysitting them, make intent explicit, and shift testing left with deterministic mocks. She also warns that new bottlenecks emerge, like decision speed and burnout.

The Agentic Commerce Stack — Ahnaf Prio, Best Buy

Aug 27, 2026 · 20:38

Ahnaf Prio, Senior Engineering Manager at Best Buy, argues agentic shopping now runs on standardized commerce primitives rather than browser automation, with 45% of agent sessions on ChatGPT and Gemini already touching shopping. He explains why screenshot/DOM agents failed and maps MCP, A2A, OpenAI's ACP, Google's UCP, and AP2 payment mandates. Merchants push product feeds because M merchants times N products does not scale; payments today use shared tokens or Google Pay. His live demo has his cat Jenny as a bakery agent on Cerebras at 3,000 tokens per second, showing checkout state transitions and an AP2 token with spend ceiling and revocation URL. He closes with evals for behavior, protocol compliance and latency, citing Chipotle's agent answering programming questions as a whack-a-mole caution.

The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph

Aug 26, 2026 · 18:16

Stephanie Jarmak, agent advocate at Sourcegraph, says developer advocacy isn't dead—the audience is now AI agents that read docs, call APIs, hit errors, and recommend tools. After going from zero commits to 12,000 in a year, Jarmak built CodeScaleBench, ran agents with and without Sourcegraph's code navigation MCP tool, and found failures like a model burning a turn on a guessed parameter. Her GEO experiments recommended Sourcegraph 65% of the time for shopping prompts but zero for the actual pain of breaking downstream services when changing shared libraries, which got a wiki-page suggestion. Her advice: enter MCP registries, keep content fresh, reduce adoption friction, and treat agent advocacy as a curb cut that clears the path for humans too.

How AI Agents Let GTM Teams Scale — Justin Joyce, Cloudflare

Aug 26, 2026 · 19:15

Cloudflare principal sales operations manager Justin Joyce argues traditional go-to-market does not scale, so his team built a three-pillar agentic system on Cloudflare Workers and Durable Objects. To scale analysis, role-specific skill files let non-SQL users query data, cutting two-hour analyses to five minutes. To scale insight, weekly summaries are drafted by one agent, verified by a second, and toned by a third, tested on every run for two to three months. For self-service, Cloudflare OS gives reps forecast briefs, QBR decks, account plans and renewal prep with centrally curated expert skills. The result is 2X efficiency, with harder problems ahead in quoting, approvals and CRM writes.

Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

Aug 26, 2026 · 18:49

Jeffrey Wang, cofounder of Exa, argues go-to-market is an AI engineering problem and details Exa's stack: an ICP dashboard classifying nearly every company in its addressable market with anticipated spend, and Request Lens alerting on meaningful customer events. The team runs about a dozen Slack agents plus Jeffbot, an AI clone of Wang trained on 760 emails—he averages 18 words and signs 'best' not 'sincerely'—and limited to drafts when others use it. He closes on three principles: agent-first means API-first, not everything should be a chatbot, and buy-versus-build is false—Salesforce exposed as MCP is arbitrarily customizable. He also notes an eight- or nine-person FDE org runs deals and builds the sales systems.

Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked

Aug 21, 2026 · 13:22

Jeff Ng, founding engineer at Unblocked, argues building agents is trivial but missing context makes them confidently wrong. Cloud primitives and frameworks have removed plumbing that six months ago took a team a quarter. In a demo, an agent enriching a Linear ticket recommended re-enabling async dispatch, missing that a support engineer had disabled it after an outage; it lacked the Slack thread and postmortem. Background agents fail silently when no human supplies that context. Ng's context engine connects docs, code, tickets, and conversations, reconciles conflicts, and returns a synthesized understanding scoped to permissions. MCP provides access, not understanding; rerunning the same agent grounded in the engine flips its recommendation from repeating the outage to preventing it.

Building Blocks for Uber’s Software Factory— Uday Kiran Medisetty & Adam Huda, Uber

Aug 21, 2026 · 18:26

Uber's Uday Kiran Medisetty and Adam Huda explain the six building blocks of their agentic software factory, which now generates more than 70% of pull requests and has doubled lines of code per engineer year over year. A model gateway handles 100 million requests a day across 800 projects with redaction of 20+ PII types and safety models under 100 milliseconds; an MCP gateway cuts token use over 40%, a skills marketplace runs 20,000 executions daily across 2,500 skills, and a context graph of 40 million entries replaces 20-30 systems. Adam shows one feature end-to-end, from Slack idea to draft PR that stops before CI, validating via simulator screenshots against Figma in an inner loop. The bottleneck, they argue, has moved to deciding whether a thing should be built at all.

Unlock Agent Autonomy: The Runtime for AI-Native Systems — Tushar Jain, Docker

Aug 20, 2026 · 22:50

Tushar Jain of Docker argues safety, not intelligence, is the blocker to agent autonomy, proposing a runtime beneath every model and harness. His evidence: a nightly agent that posted a private report as a PR, and an incident agent that widens access from logs to Slack to GitHub. The runtime has three pillars: containment with controls outside the agent's boundary, just-in-time tools scoped per task, and intent-based access that refuses off-task asks like email. Docker's new SPX tool runs agents in micro-VMs with injected stub credentials and scoped sandboxes locally, in the cloud, or in a VPC. Demos split a PR-review and Notion-writing job across two sandboxes, fan out to six parallel sandboxes, and show an early prototype auto-creating a scoped sub-sandbox for GitHub.

IT Admin for the AI Workforce — Sarthak Aggarwal, Decawork

Aug 20, 2026 · 16:17

Sarthak Aggarwal, co-founder of Decawork, argues enterprises are onboarding a second workforce of AI agents, and the hard part is making them safe to employ: identity, delegated authority, scoped access, and revocation. He cites EchoLeak, a zero-click CVE where an external email entered Microsoft 365 Copilot's context and pulled data out, and Replit, where a coding agent ignored a code freeze, deleted production data, and misrepresented it. Guardrails are telemetry, not boundaries. The fix is privilege separation: a planner turns authenticated intent into a logged plan before seeing evidence; an executor runs that plan with short-lived capabilities and no standing credentials. OAuth token exchange has the right shape, but no agent identity standard exists; model proposes, policy decides.

Your Agent Evolved. Your Evals Didn't. — Ameya Bhatawdekar, Braintrust

Aug 20, 2026 · 24:13

Ameya Bhatawdekar, Field CTO at Braintrust, argues that evals are the durable asset across AI replatformings and must move with each architectural generation. He traces five generations—single prompt, retrieval chain, ReAct loop, workflow graphs, and the return of loops with newer models—grounded in a site reliability agent, showing how each added failure surfaces: parsers, retrievers, branch logic, node contracts, classifier misfires, and trajectory variance. New metrics like pass@k (succeeds at least once in k attempts) measure capability, while pass^k (how many of k succeed) measure reliability. He urges harvesting production data into evals via the flywheel, and says Braintrust's Topics clusters production data to surface unanticipated failure modes.

Generative Video at the Speed of Light — Keegan McCallum, uRun

Aug 18, 2026 · 8:43

Keegan McCallum, founder of inference provider uRun, argues that generative video's interesting axis is no longer quality but efficiency and long-horizon generation. He shows Helios, a distillation of Wan 2.1 14b, generating clips in real time for ~one-hundredth the cost of a slower frontier-quality clip, and notes at least 40 real-time/long-horizon models released this year. Ten dollars buys three hours of continuous generative video, and fifty buys fifteen; this unlocks magic-mirror webcam transformations, visual mediums for people who don't think in text, and content creation where you steer a generation in under a second. The hard part is serving: global GPUs, WebRTC with ICE/TURN, and synchronized streaming pipelines; uRun is building a React component, Python runtime, and MCP/CLI.

Security Firewall for Agents — Ryan Dahl, Deno

Aug 17, 2026 · 19:06

Ryan Dahl, CEO of Deno, argues agents must be treated as untrusted software and introduces Claw Patrol, an MIT-licensed proxy that parses every byte leaving an agent below the HTTP layer. At Deno Deploy, agents with write access to Postgres, Kubernetes, ClickHouse, and AWS can be prompt-injected through the support system, so Opus refusing to delete the users table is not enough. Claw Patrol blocks destructive actions even when an agent spawns psql through an EKS endpoint, using HCL rules checked into Git, holds credentials so agents never see them, and can route actions to an LLM judge or Slack approval. A demo shows Codex in yolo mode trying to delete the users table and being blocked. It also includes a unit test system with fixture requests to ensure rules work.

From RL to IRL — Gaurav Mishra, Amazon AGI Lab

Aug 14, 2026 · 17:46

Gaurav Mishra of the Amazon AGI Lab explains why RL for computer-use agents works in games but breaks in real life, and how his team turns failures into training data. He shows early browser-training runs where an agent guesses its password and locks the account, and clicks a sponsored button styled like the submit button, landing elsewhere. The talk catalogs partial observability, irreversibility, expiring credentials, and ambiguous success, then proposes flight-school sandboxes, process reward models, calibrated confidence, and adversarial tasks. A later trajectory shows the agent recognizing the sponsored button, refusing to guess the password, and handing off to the user, echoing the talk's point that the difference between a demo and a product is what happens after the first failed click.

The Dark Arts of Web Automation: Teaching Agents to Use Websites Like Humans — Corey Gallon, Rexmore

Aug 14, 2026 · 21:38

Corey Gallon (Rexmore) argues that an agent driving Chrome via the Chrome DevTools Protocol is just a 'meat bag with a mouse'—and details his open-source Chrome Agent tool. Preparing the talk earned him an OpenAI ban threat after his agent cleared Cloudflare Turnstile, MT Capture, Lemon, and reCAPTCHA v2 with no human in the loop. He argues for CLI over MCP: both succeeded ~83% in an Arise AI study, but CLI took 7 turns and under a minute versus MCP's 71 round trips and 8 minutes, and CLI can be 75x cheaper in tokens. The method is a sense/act/verify loop up a three-rung ladder: synthetic clicks, trusted CDP input, then human mouse paths with jitter, deliberate overshoot. For reCAPTCHA, deterministic code drives and rearms each round while the agent names grid tiles, as speed beats expiration clocks.

Computer-use models will agentify the web, not APIs — Dhruv Batra, Yutori

Aug 14, 2026 · 21:00

Dhruv Batra of Yutori argues computer-use models, not APIs, will agentify the web's long tail. The head may expose endpoints, but 200 million active sites won't add MCP servers: restaurants post JPEG PDF menus and school districts answer procurement via FOIA requests scanned onto Google Drive. Reading HTML fails because scores and stock status load asynchronously and render as pixels, so the browser is a rendering engine: pixels are the source of truth, the bitter lesson for web agents. His Navigator model takes screenshots in, clicks out, writes JavaScript when faster, and verifies on screen; it hits 97% human eval on Mind2Web (8 of 300 wrong) at 80 cents per task versus $2.30. That endpoint will be another layer: simulated browsers clicking and returning structured results.

Claude Managed Agents and Evolution of Agentic Surfaces — Gagan Bhat & Isabella Kai He, Anthropic

Aug 11, 2026 · 31:24

Gagan Bhat and Isabella Kai He of Anthropic's Applied AI team explain how agentic surfaces evolved from the Messages API to the Claude Agent SDK and now Claude-managed agents, arguing that harnesses encode assumptions about model limits that go stale as models improve. They detail decoupling the brain (agent loop) from the hands (tool execution sandbox), which cut time to first token by 60% at P50 and over 90% at P95, and made failures recoverable via durable session logs. The session log also powers observability, context recovery, and 'dreaming,' a batch process that rewrites agent memory for self-improvement. They cover production lessons: keeping credentials in vaults, self-hosted sandboxes for VPC control, MCP tunnels for private servers, and the 'outcomes' feature that uses a grader agent to enforce success criteria.

How Codex Works — Dominik Kundel, OpenAI

Aug 10, 2026 · 20:55

Dominik Kundel, an OpenAI engineer, explains the internals of the Codex agent harness, which is open source under Apache 2 and written in Rust. He details how context construction caps available skills at 2% of the context window and uses deferred tools with tool search to manage size and cost. For actions, Codex relies on an apply patch tool for file edits, a shell tool with ripgrep, and sandboxes: seatbelt on macOS, bubblewrap on Linux, and a custom open-source Windows sandbox. To reduce approval fatigue, an auto-review subagent with read-only permissions judges actions against user authorization and risk taxonomies. Speed improvements come from websocket mode in the responses API, which sends only changed items instead of full state, crucial when GPT 5.3 Codex Spark hit 1,000 tokens per second on Cerebras. Long-horizon goals work via a continuation prompt until the model calls an update goal tool, favoring concrete verifiable objectives, and auto compaction maintains performance…

MCP Tasks (async): Why Aren't Any Agents Supporting Them? — Cornelia Davis, Temporal

Aug 2, 2026 · 23:54

Cornelia Davis, a distributed systems veteran and technologist at Temporal, argues that MCP Tasks remain unsupported because the V1 spec was experimental and deeply involved, and she walks through the V2 redesign that makes long-running, durable tool calls practical. Using an invoice-processing flow with human-in-the-loop approval, she demonstrates that a task survives client disconnects, server crashes, and network blips because the spec says once launched, a task must be durable. She explains V1's pain points: a stateful task_list endpoint with no filtering that cannot scale to a million tasks, and a task_result tunnel that requires a long-lived connection to deliver input-required events. V2 replaces that with a stateless core, makes tasks an extension, removes task_list, and lets clients send updates into a task via a new endpoint, while keeping the lifecycle state machine unchanged. She warns that the spec only says clients 'should' persist task IDs, and without that there is no way to recover a task; her ongoing work covers a notifications protocol for scale and shipping all of this in Fast MCP.

Your Finance Agent's Bottleneck Is You — Ramana Siddanth Emani, Auditoria AI

Jul 30, 2026 · 13:42

Ramana Siddanth Emani, data scientist at Auditoria AI, argues that the bottleneck in shipping production finance agents is the developer in the loop, not the model or GPUs. He presents harnessing coding agents across parallel git worktrees (50 with 48GB RAM) to automate bug fixes from QA and Jira tickets, with sub agents pulling traces, logs, running tests, and creating PRs, needing human input only at task assignment and final verification. In the finance sector, regulation demands human oversight, so agents handle the grind while developers focus on verification. Emani advocates for recursive self-improvement where agents analyze bottlenecks and upgrade loops; after a month, a developer can simply type 'fix this bug' and the agent handles everything, allowing developers to step out of the loop while remaining final verifiers.

Your Moat Is Your Data Model — Mike Phipps, Gates Foundation

Jul 22, 2026 · 20:30

Mike Phipps of the Gates Foundation argues that as AI commoditizes frontends and agent frameworks, the durable moat is your data model and the tacit knowledge of how your questions are answered. At the foundation, he and his team modeled 25 years of grantmaking—$7 billion a year across 2,000 grants and 4,000 people—into a single Neo4j knowledge graph served to Claude through one MCP server. The graph is built for agents, not dashboards: hierarchies become traversable paths, and unstructured documents are chunked, tagged, and mapped to structured entities at ingestion. Phipps details the curation pipeline, engaging data owners to capture reporting conventions and safeguard constraints, and explains how the graph connects siloed systems (funding, management, org charts) with unstructured meeting documents. Retrieval evals with LLM-as-judge measure pass-at-one and stability, surfacing gaps that feed back into the data model. The talk makes the case that a small team's efforts compound in the data layer, not the layers above it, offering a practical architecture for enterprise agentic retrieval.

CrabRAG: Why Automated Assistants Need Graph Memory, Not More Tokens — Stephen Chin, Neo4j

Jul 22, 2026 · 20:42

Stephen Chin of Neo4j introduces CrabRAG, a graph-based memory system that outperforms vector databases for AI agent reasoning. He demonstrates that markdown-based memory wastes over 100,000 tokens per round and that vector similarity fails at multi-hop questions. Using a home lab digital twin, he shows a graph agent correctly identifies his daughter's Minecraft server running outdated OS and exposed management ports, while the vector agent returns vague answers. Chin explains that graphs store relationships and enable precise, explainable, and auditable results, and that Claude can write Cypher queries for graph traversal. He announces his book 'GraphRAG: The Definitive Guide' and free training at Neo4j's Graph Academy.

Full Workshop: Better Auth — Paola Estefania, Better Auth

Jul 21, 2026 · 40:56

Paola Estefania of Better Auth presents Agent Auth, a protocol giving AI agents their own identity and fine-grained capabilities instead of impersonating users. She argues agents need principal-level security: discovery via a directory, authorization with scoped capabilities (e.g., read vs. send email), and identity through per-agent private keys for audit trails and revocation. A live demo shows an MCP-connected agent reading Gmail, requesting send-email permission, and being revoked mid-action. The protocol, now an open draft with a Better Auth plugin, treats agents as principals — contrasting with OAuth scopes and AI gateways that lack per-agent identity. Paola invites contributions, especially for enterprise IAM-style policies.

It's 10pm. Do You Know Where Your Agents Are? — Kim Maida, Keycard

Jul 20, 2026 · 23:02

Kim Maida of Keycard argues that standard API keys dangerously overprivilege AI agents, enabling incidents like a night-shift agent dropping a production Postgres database because its kitchen-sink credential allowed it. Her fix uses OAuth token exchange (RFC 8693) to mint a fresh, short-lived, scoped token per tool call, evaluated against policy before the credential exists. This prevents leaks, replays, or theft—the drop request never receives a credential. It works across CLI agents, MCP servers, and any OAuth provider, and strengthens human-in-the-loop approval by checking operator roles against policy, preventing consent fatigue bypass. By chaining user and agent identity through a security token service, every action is attributed and delegation is narrowed at login and per call.

Skills are the New SDKs - Elvin Aghammadzada, DataRobot

Jul 20, 2026 · 26:40

Elvin Aghammadzada of DataRobot argues that enterprise AI platforms must become 'teachable' through a skill layer—versioned, task-specific packages that encode operational knowledge for coding agents. He contends that context windows degrade after 25% usage (a phenomenon called 'context rot'), making progressive disclosure via skills critical for maintaining agent performance. Skills expose only metadata (<100 tokens) until activated, while MCP servers handle heavy resource isolation; skills can also self-modify and spawn MCP servers. With 26+ platforms (Claude Code, Codex, Copilot) supporting skills and 85,000+ published, the ecosystem is creating a 'fluency moat' where platform value compounds with each skill. However, LLM-generated skills currently hurt performance, and marketplaces lack verification controls, echoing early NPM risks. The episode positions skills as complementing MCPs, not replacing them, and urges versioning and testing as software.

Medic for Apache Spark - First Aid for Failing Jobs - Drasko Profirovic, Pinterest

Jul 20, 2026 · 11:21

Drasko Profirovic, a Staff Engineer at Pinterest, presents Medic for Apache Spark, an agentic diagnostics tool that automatically troubleshoots Spark job failures by ingesting logs, correlating context, and producing human-quality diagnoses and actionable recommendations in minutes. The talk covers the journey from a single React agent with unsustainable prompt tuning to a multi-agent architecture built on LangGraph's deep agent library, where specialized agents handle triage, research, and healing. Key improvements include an exception classifier pipeline that filters benign exceptions from logs, converting raw time-series metrics into annotated graphs for token efficiency, and an end-to-end test harness that snapshots production state for offline evaluations. Profirovic shares lessons on handling ambiguity, reducing hallucinations, and balancing automation with human oversight, plus unexpected failure modes that informed iterations. The system now extends to optimizing Spark SQL…

Agents Need Receipts, Not More Tool Calls - Armanas Povilionis, Alithea Bio

Jul 18, 2026 · 19:36

Armanas Povilionis of Alithea Bio argues that agents need verifiable receipts, not more tool calls, and introduces Froglet, an open-source protocol for agent-to-agent compute. Froglet enables agents to discover, negotiate, execute, and receive signed, tamper-proof receipts for external services, reducing setup to 2,000 tokens and minutes. The protocol integrates with MCP, OpenClaw, and NemoClaw, and supports payment rails, identity, and workload hashes. In a live demo, he shows Claude using Froglet to publish an 'add two numbers' service locally and remotely, then invoke it to get result 12, with a signed deal ID. Froglet aims to make scientific collaboration repeatable and consistent across organizational boundaries without requiring uniform software stacks.

Modern Post-Training: A Deep Dive — Will Brown, Prime Intellect

Jul 13, 2026 · 46:52

Will Brown of Prime Intellect details the company's open-source ecosystem of post-training tools, including the verifiers and prime-rl libraries, arguing they enable efficient and affordable training of frontier agentic models for enterprises. Verifiers V1 decomposes environments into tasks, harnesses, and runtimes using a decorator pattern and Pydantic, supporting group rewards like conciseness bonuses. Prime-RL is an asynchronous reinforcement learning framework that allows long-horizon coding rollouts to overlap, achieving a GLM-5 step on 28 nodes in under 5 minutes for 131k context, with a 1,000-step run costing roughly $50k. The framework supports custom algorithms including on-policy distillation, GRPO, and self-distillation via decomposable loss and algorithm classes. Prime Intellect's Lab platform offers hosted multi-tenant LoRA training live now, with full fine-tuning arriving soon, enabling enterprises to develop environments on CPU and push them to the cloud for scalable post-training.

I Run a Fleet of AI Agents Across Three Machines. Here's What Broke. - Kyle Jaejun Lee, KRAFTON

Jul 8, 2026 · 9:11

Kyle Jaejun Lee runs a fleet of AI coding agents across three machines daily and reveals the scaling failures that emerged from a hierarchy of CEO, VP, manager, and worker agents. To overcome his own attention bottleneck, he separated context into entity-specific workspaces on disk and replaced context compaction with full resets that read handoff files. When moving beyond one machine, five things broke: agents doing work instead of delegating, TMUX panes too crowded to read, out-of-memory crashes from stacked Claude Code processes, colliding Git credentials across workspaces, and the MacBook dying mid-task. He offloaded long-running work to always-on Linux boxes, used Git commits and SSH to move context between machines, and consolidated review gateways onto a single always-on machine with Discord as a unified router. Unsolved challenges include consistent credentials, local-only tools, and resource scheduling—he plans to layer his orchestration on top of Kubernetes to handle compute, secrets, and tools.

The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents

Jun 29, 2026 · 30:38

Justin Schroeder of StandardAgents argues that domain-specific agents—small, focused AI agents each limited to a single domain—will outperform general-purpose agents that rely on accumulating tools and context via inheritance, and that composition of such agents is the key to building practical, cost-effective AI systems. He defines an agent as deterministic software harnessing non-deterministic model outputs, then critiques the current approach of piling tools and skills into a single agent (inheritance), proposing instead a coordinator agent that delegates to many specialized sub-agents (composition), each with its own minimal context, system prompt, and tools. He claims these domain-specific agents achieve over 80% token efficiency, enable use of cheaper small models (e.g., DeepSeek V4 Flash is 137x cheaper than a frontier model), improve safety by limiting capabilities, and scale easily. Schroeder predicts rapid adoption through 2026-2027, with multi-agent orchestration becoming mainstream and tokens no longer getting cheaper—up 29% when adjusted for IQ. He describes an ideal agent architecture with hooks, rules, and recursive sub-agents, and invites listeners to try…

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

Jun 28, 2026 · 17:36

Angie Jones describes how she transformed Block's 3,500-person engineering org into an autonomous one using AI agents, starting with a maturity model and an AI champions program of 50 hand-picked engineers. She details making repos AI-ready with context and rules files, then enabling delegation from Slack, Jira, and GitHub, which boosted AI-authored code by 69% and automated PRs 21x. Multi-agent parallelism required cloud workspaces and a company world model across 25,000 repos, culminating in BuilderBot that allowed anyone to @mention it for fixes. However, the success led to layoffs, raising existential questions about the human cost of autonomous engineering.

Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS

Jun 28, 2026 · 17:47

Erik Hanchett (AWS) argues that spec-driven development—writing markdown specification files before any code—produces higher quality code by guiding AI coding assistants, which he compares to 'AI interns' that need explicit direction. He introduces Kiro, AWS’s new AI IDE and CLI, which offers a spec mode that generates requirements documents, design documents with mermaid diagrams, property-based tests using Fastcheck, and a task list. In a demo building a movie website, Kiro created an MVP-focused implementation verified by property tests. Hanchett also highlights integrating external data via the Model Context Protocol (MCP) and stresses the importance of human review. He advises balancing context in steering docs and using on-demand skills to control the workflow.

The 100-Tool Agent Is a Trap - Sohail Shaikh & Ankush Rastogi, Prosodica

Jun 28, 2026 · 28:27

Sohail Shaikh and Ankush Rastogi of Prosodica argue that the common 'Fat Agent' architecture—loading every tool schema into every prompt—causes latency, cost, and accuracy collapses as tool catalogs grow. Their benchmarks show tool selection accuracy dropping from 78% at 10 tools to 13.6% at 741 tools, while their Semantic Tool Router pattern stays above 83% across the same range. The router uses Just-in-Time Context Injection, embedding tool descriptions offline, retrieving 3–5 relevant tools per query at runtime, and injecting only those schemas into the model call. This reduces input tokens by up to 99% (from 127k to ~1k tokens) and cuts time-to-first-token latency by up to 90% in high-tool-density scenarios. They present a production checklist and emphasize that routing pays off once beyond 50 tools, with K=5 as a strong default, but warn of router misses and weak descriptions needing fallbacks and monitoring.

Why MCP and ChatGPT Apps Use Double Iframes — Frédéric Barthelet, Alpic

Jun 15, 2026 · 20:11

Frédéric Barthelet, CTO of Alpic, explains why ChatGPT and other MCP hosts render third-party app UI inside a double iframe. He traces how simpler approaches fail: `srcdoc` shares the parent origin, letting CSP block scripts and risking data access; sandboxing removes origin storage; and `allow-same-origin` recreates the escape. The resulting double iframe—an outer iframe from a controlled subdomain loading app HTML via `srcdoc` into an inner frame—ensures isolation and prevents cross-app storage collisions. Barthelet warns developers must declare every external domain their view uses in MCP app metadata or face submission rejection, and demos Skybridge's CSP inspector that diffs declared domains against actual network calls.

The agent-ready web: Simplify user actions with WebMCP — Tara Agyemang, Google

Jun 11, 2026 · 21:34

Tara Agyemang from the Google Chrome team introduces WebMCP, a proposed web standard that replaces brittle DOM scraping with structured tools for AI agents. She explains two implementation paths: the declarative API (adding HTML attributes to forms) and the imperative API (registering custom JavaScript tools). A live demo shows a concert ticket purchase completed in three tool calls: search, open page, purchase. WebMCP is in early preview on Chrome 146, with an eval CLI and inspector extension available for testing.

Self Driving Products: Product Signals to Pull Requests — Joshua Snyder, PostHog

Jun 10, 2026 · 15:39

Joshua Snyder of PostHog explains how they're building a pipeline that turns product signals—errors, Slack messages, session replays—into automated pull requests. He reveals that off-the-shelf embedding models cluster signals by structural similarity, so they embed LLM-generated queries instead. He argues specificity determines whether the agent produces a useful PR, with error tracking being immediately actionable while Slack and replay usually are not. He advises starting with costly agents to discover patterns, then collapsing expensive steps into one-shot calls.

Why Eval++ Is the Next Great Compute Primitive — Sunil Pai & Matt Carey, Cloudflare

Jun 8, 2026 · 24:51

Matt Carey and Sunil Pai from Cloudflare's agents team argue that Durable Objects — stateful serverless with 15ms London latency — and Dynamic Workers — a safe, sandboxed eval for LLM-generated code — form the next compute primitive for AI agents. They explain how Durable Objects enable resumable streaming, multi-tab sync, and background scheduling out of the box without distributed systems engineering. Dynamic Workers allow running generated JavaScript strings in isolated sandboxes, reclaiming 30 years of avoided eval. The pair tease upcoming talks: one on collapsing Cloudflare's 2,600 API endpoints into a 1,000-token MCP tool, and another on a coding agent harness built entirely on Workers that they are already shipping.

Building Interactive UIs in VS Code with MCP Apps — Marlene Mhangami & Liam Hampton, GitHub

Jun 6, 2026 · 16:06

Marlene Mhangami and Liam Hampton from GitHub explain MCP Apps, which let server tools return rich interactive UI components inside VS Code Chat via sandboxed iframes, replacing plain text responses. They demonstrate a flame graph profiler that profiles a Go app running bubble sort and Fibonacci, rendering an interactive flame graph directly in the chat window. The MCP server returns data and a resource reference to bundled HTML; VS Code fetches and renders it, enabling live interaction like clicking and querying without leaving chat. Use cases include data exploration, e-commerce checkouts (Shopify), and interactive diagrams (Excalidraw). The iframe is sandboxed for security—like putting a hamster in a cage to prevent it from modifying VS Code settings or calling external APIs. Marlene and Liam walk through building an MCP App from scratch using a skill from the model context protocol repository.

Beyond Components: Designing Generative UI for MCP Apps — Ruben Casas, Postman

Jun 3, 2026 · 16:58

Ruben Casas from Postman argues that AI models can now write better frontend code than he can—his prompt to rewrite his blog produced a search box with blur animation and accessibility out of the box—yet most agent UIs still invoke static prebuilt components. He presents three levels of UI generation: static components (AG UI, Goose) passing props to predefined React elements; declarative UI where the model generates JSON or YAML for a rendering engine (e.g., Vercel's JSON Render), which he deems the current ideal balance; and fully generative UI where the model writes HTML, CSS, and JavaScript on demand, as in his weather agent that does so in one tool call. The key obstacle is trust, necessitating sandboxing, and MCP apps with their double iframe default are the best delivery mechanism. He likens today to early TV—radio shows with cameras—and predicts the future lies beyond components in collaborative human-agent interfaces on shared canvases, as seen with the Skeletro MCP app.

Stop babysitting your agents... — Brandon Waselnuk, Unblocked

May 26, 2026 · 18:54

Brandon Waselnuk of Unblocked argues that the bottleneck for AI coding agents is not access (MCPs, tools) but understanding—they need a context engine that builds a research packet from codebase, Slack, PRs, and org structure before generating code. He debunks three myths: naive RAG suffers from satisfaction of search, more MCPs don't provide reasoning, and million-token windows don't enable effective reasoning. His team's context engine reduced a Zendesk integration task from 2.5 hours and 20.9M tokens (with MCPs only, but code that would have broken production) to 25 minutes and 10.8M tokens, earning a senior engineer's approval with one nitpick. Three hard lessons: optimize for understanding not access, resolve conflicts rather than hide them (e.g., Slack thread where CTO says code is wrong), and never cache answers because context changes daily. He demonstrates a social graph tool (open-sourced Monday) that maps engineers to code areas and collaborators, and shows a demo where the agent's MCP calls the context engine to generate a plan covering factory patterns, library modules, and client registration.

How Google DeepMind Runs Agents at Scale — KP Sawhney & Ian Ballantyne, Google DeepMind

May 24, 2026 · 25:13

Ian Ballantyne and KP Sawhney from Google DeepMind explain how the company scales its agentic platform anti-gravity, featuring a Darwinian skills library and quota management prioritizing paying customers. KP explains deep research currently passes huge context blobs, but his focus is replacing them with a shared file system, enabling artifact generation like infographics. Token-hungry agents require brute-force quota limits; 24/7 monitoring stops spikes, and internal teams face worse quotas than customers. Observability uses a custom web app tracing agent trajectories to raw predict requests. The skills library relies on contributions from domain experts, with survival through evaluation in sandboxed environments. For code review, per-language auto-review models fine-tuned on style guides automatically comment on PRs.

Anthropic Workshop: Build Agents That Run for Hours — Ash Prabaker & Andrew Wilson

May 18, 2026 · 1:15:40

Anthropic's Ash Prabaker and Andrew Wilson detail how to build agents that run for hours by replacing self-evaluation with adversarial evaluator agents that use Playwright to test live apps and grade subjective output via rubrics. They explain that context compaction doesn't cure coherence drift, so structured handoffs between fresh context windows are essential. The generator and evaluator negotiate testable sprint contracts before building, and the planner provides high-level specs without overspecifying technical details. They show that a solo Claude Code session built a retro game maker that looked complete but failed in play mode, while the adversarial harness produced a fully functional app with live physics and AI features. Key takeaways include reading traces as the primary debug loop, deleting harness components as models improve, and using file-system state for long-running agents.

Make your own event-sourced agent harness using stream processors — Jonas Templestein, Iterate

May 14, 2026 · 1:04:27

Jonas Templestein and Misha from Iterate introduce an event-sourced agent harness built on stream processors, arguing that agents should be modeled as an append-only event log with a synchronous reducer for state and an after-append hook for side effects. They demonstrate how every action—streaming chunks, tool calls, errors—becomes an event, enabling full debuggability and replay without re-running LLM calls. The key innovation is a "dynamic worker configured" event whose payload is a JavaScript processor; appending it to any stream instantly turns that stream into an AI agent with no server or dependencies. This allows processors from different authors and languages to compose on the same stream, and a safety checker can inject context within 200ms without blocking the agent. The hosts emphasize eventual consistency over before-hooks, and show deployment via Cloudflare Workers or by simply subscribing from any HTTP client.

Malleable Evals: Why Are We Evaluating Adaptive Systems with Static Tests? — Vincent Koc, OpenClaw

May 12, 2026 · 15:05

Vincent Koc argues that AI applications are adaptive systems, yet evaluations remain static datasets—a problem he calls 'eval calcification.' In this talk at AI Engineer, he explains that 80% of an agent’s work is stable, but the 20% that constantly shifts as users change is what breaks businesses. He proposes treating evals as living code: agents that self-curate test suites from their own traces, integrate telemetry in the loop so the harness detects and self-corrects from failures, and define end states rather than right answers. Koc draws on his work with Comet and the OpenClaw harness, where the harness itself adapts and changes. The result is evaluations that are not a fixed dataset but a self-optimizing system that grows with the application.

Demand-Driven Context: A Methodology for Coherent Knowledge Bases Through Agent Failure

May 5, 2026 · 1:08:15

Raj Navakoti, a staff software engineer at IKEA, presents a demand-driven context methodology for building coherent knowledge bases by letting AI agents fail on real problems and surfacing missing institutional knowledge. He argues that enterprises should shift from pushing monolithic documentation to a pull approach where agents reveal undocumented tribal knowledge through repeated failures on incidents and Jira tickets. Using a framework with skills, rules, and hooks, he demonstrates how agents can gradually improve confidence scores (from 1.4 to 4.4 over 14 incidents) by documenting discovered context blocks. Navakoti introduces a context gap scanner that automatically analyzes work items against existing documentation to identify critical gaps, outdated information, and duplications. He advocates storing curated knowledge in GitHub for version control and PR-based collaboration, and emphasizes that this approach helps teams know the unknown, enabling agents to manage knowledge rather than just consume it.

Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase

May 4, 2026 · 1:18:41

Pedro Rodrigues, AI tooling engineer at Supabase, demonstrates how to write, test, and iterate on Agent Skills to make agents actually good at real systems like Supabase's performance-review app. He explains progressive disclosure—skills load only a short description first, letting the agent decide when to pull in full instructions—and shows that combining skills with MCP tools gives agents both context and actions. In a live demo, Rodrigues reveals a common failure: creating a database view without a security invoker flag bypasses row-level security, letting all users see everyone's salary data. Adding a skill with security checklists guides the agent to include the flag, fixing the bug. He then introduces an eval-driven development cycle using an eval.json file and an LLM-as-judge to automate testing across conditions with and without the skill, cautioning that writing accurate evals is tricky because non-deterministic outputs can mislead. The session offers a practical framework for validating skills, avoiding pitfalls, and measuring what actually improves agent behavior.

One Login to Rule Them All: Cross-App Access for MCP — Garrett Galow, WorkOS

Apr 28, 2026 · 23:24

Garrett Galow from WorkOS introduces Cross-App Access (XAA) for MCP, solving the problem of repeated OAuth consent screens when connecting agents to multiple services. The flow leverages a three-way trust between the MCP client, server, and an Identity Provider like Okta: a single SSO login issues an IDJag token that is exchanged for short-lived access tokens across all MCP servers without manual intervention. A demo shows Figma automatically connecting after an Okta login. The approach improves security—if the IdP session is revoked, tokens cannot refresh—and requires minimal IT setup: granting permission for the client to request server access. Currently only Okta supports XAA; Azure/Entra does not yet. The session also notes that authorization scopes are not handled by default but are a future consideration.

Gateways are All You Need — Karan Sampath, Anthropic

Apr 27, 2026 · 17:48

Karan Sampath, an engineer at Anthropic, argues that enterprises face a 'three-headed hydra' of observability, access control, and security when adopting MCPs, and proposes that unified gateways are the solution. He explains that current decentralized MCP deployments create bottlenecks, limiting enterprises to single-digit MCPs due to opaque usage, lack of role-based scoping, and data exfiltration risks. The gateway, a middleware layer handling auth, proxying, routing, and tunnels, establishes a root of trust. Core components include OAuth integration, a developer CLI, and an internal sub-registry. Benefits include multi-surface integration (Claude.ai, Claude Code), standardized access control, faster iteration without repeated security reviews, and scalable credential management. Sampath envisions decoupling agent architecture from data layers, making gateways invariant as agent surfaces evolve.

MCP = Mega Context Problem - Matt Carey

Apr 25, 2026 · 22:42

Matt Carey from Cloudflare argues that the real bottleneck in connecting agents to APIs via MCP is the context window, not the protocol itself. Cloudflare’s OpenAPI spec is 2.3 million tokens; naive tool generation would consume 1.1 million, overwhelming any agent. To solve this, Carey advocates progressive discovery: CLIs (requiring shell access), tool search (loading only relevant tools), or codemode—letting agents write code against a typed SDK and execute it in Cloudflare’s isolated Workers sandbox. This sandbox provides safe, programmable guardrails (no secrets, limited network access), allowing agents to access all 2,600+ Cloudflare API endpoints from a single MCP server. Carey predicts that as agents become code generators, infrastructure primitives like WorkerD, Deno, and Pydantic will proliferate, and MCP servers will become a lightweight middleware flag (e.g., `MCP: true` in Next.js). The talk demonstrates read-only access to the entire Cloudflare API from an MCP client, showing codemode in action.

Full Workshop: Build Your Own Deep Research Agents - Louis-François Bouchard, Paul Iusztin, Samridhi

Apr 20, 2026 · 1:57:03

Louis-François Bouchard, Samridhi Vaid, and Paul Iusztin built a combined system of a deep research agent and a deterministic writing workflow to automate high-quality technical content creation, trading flexibility for control. The research agent uses an MCP server (FastMCP) with tools for Google search, YouTube analysis, and report compilation, powered by Gemini and orchestrated by Claude Code. The writing workflow is a static pipeline that loads a user guideline, writing profiles (structure, terminology, character), and few-shot examples into a system prompt, then applies an evaluator-optimizer loop: a reviewer outputs structured pydantic objects with profile/location/comment to flag violations, and the editor applies them in priority order (guideline > research > profile). They stress that research demands autonomy while writing needs constraint, and show how to avoid AI slop by banning specific words and using profiles. For observability, they use Opik to capture all traces, and for evals they built a 20-sample dataset from real LinkedIn posts, split into train/dev/test, to calibrate an LLM judge (binary pass/fail) that they run on dev and test splits to measure F1 and detect…

The Future of MCP — David Soria Parra, Anthropic

Apr 19, 2026 · 18:46

David Soria Parra from Anthropic argues that MCP (Model Context Protocol) is the key to connecting agents to tools and data in production, with 110 million monthly downloads—outpacing React's growth at the same stage. He lays out a 2026 connectivity stack combining Skills, MCP, and CLI/Computer Use, each suited for different needs, and emphasizes that best agents will use all three seamlessly. To improve client harnesses, he introduces Progressive Discovery—deferring tool loading via Tool Search to reduce context usage—and Programmatic Tool Calling, where models write scripts to compose tool outputs efficiently. Upcoming MCP protocol improvements include stateless transport (with Google) for easier scaling, async agent-to-agent tasks, enterprise features like Cross App Access and Server Discovery via well-known URLs, and a Skills-over-MCP extension for shipping usage instructions with servers. He calls for community feedback on these directions.

One Registry to Rule them All - Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca

Apr 10, 2026 · 22:47

Amplifon's AI transformation led to the Amplify program, for which Quantyca built an enterprise-grade registry system for MCP servers and A2A agents. Sonny Merla, Mauro Luchetti, and Mattia Redaelli explain how three registries—MCP, A2A, and use case—are linked via a catalog to provide full lineage, including ownership, environment, authentication, cost attribution, and use case linkage. The solution uses an AI gateway for unified LLM access with Entra ID authentication and budgeting, and provides template repositories on GitHub with CI/CD pipelines that automatically publish agent cards and server metadata to the registries. This enables discovery, governance, and impact analysis across 26 countries and multiple teams, letting developers focus on business logic while avoiding reinventing security and deployment infrastructure.

Your Insecure MCP Server Won't Survive Production — Tun Shwe, Lenses

Apr 8, 2026 · 24:34

Tun Shwe and Jeremy Frenay from Lenses.io argue that poorly designed MCP servers are inherently insecure, and propose five core design principles—shrink attack surface, constrain inputs, treat docs as defense, return minimal data, minimize blast radius—that also thwart OWASP's MCP Top 10. They highlight that standard I/O fails under load (20 of 22 requests failing with 20 concurrent connections), forcing a shift to streamable HTTP with OAuth 2.1. Jeremy details Dynamic Client Registration (DCR) and the newer Client ID Metadata Document (CIMD) approach, noting DCR's vulnerabilities (phishing, non-portable registrations) and CIMD's stronger identity verification since November 2025. Beyond OAuth, enterprise deployment requires tool-level RBAC, data masking for PII, full audit logs, and end-to-end tracing to meet regulations like the EU AI Act.

Bending a Public MCP Server Without Breaking It — Nimrod Hauser, Baz

Apr 8, 2026 · 40:50

Nimrod Hauser, founding engineer at Baz, presents a hands-on guide to adapting third-party MCP servers for production, using Playwright's MCP server as an example. He demonstrates five best practices—curation, wrapping descriptions, deterministic guardrails, composing new tools, and treating tools as deterministic functions—to transform a brittle setup into resilient infrastructure. Hauser shows how curating tools from 21 to 16 reduces context load, while enhanced descriptions guide agents to use accessibility snapshots before clicking. Deterministic path validation prevents agents from saving screenshots outside a designated folder, and a new 'evidence tool' with tailored instructions improves spec review accuracy. The talk culminates with Baz's Spec Reviewer, an agentic code review tool that compares requirements against implementation, successfully passing a test after applying all optimizations.

Identity for AI Agents - Patrick Riley & Carlos Galan, Auth0

Jan 14, 2026 · 1:22:12

Patrick Riley and Carlos Galan from Auth0 (Okta) present new identity and access management features for AI agents, including Async Auth, Token Vault, and MCP integration. Token Vault securely stores and refreshes upstream resource tokens, enabling agents to access APIs on behalf of users. Async Auth allows agents to request user approval for risky operations, such as placing an order, via out-of-band notifications like push to the Guardian app. They demonstrate a Next.js chatbot that uses Token Vault to read a user's stock portfolio and Async Auth to require explicit consent before executing trades. The MCP server is secured with OAuth 2.0, supporting Dynamic Client Registration (DCR) and fine-grained scopes. The solution bridges user identity, agent identity, and upstream API authorization, ensuring agents can act autonomously but with user-defined boundaries.

Your MCP Server is Bad (and you should feel bad) - Jeremiah Lowin, Prefect

Jan 12, 2026 · 54:33

Jeremiah Lowin, CEO of Prefect and creator of FastMCP, argues that most MCP servers are poorly designed because they are simply REST API wrappers, leading to confused LLMs and wasted tokens. He presents a framework for agent-native product design based on three dimensions—discovery, iteration, and context—and offers five actionable principles: outcomes over operations, flatten arguments, treat instructions as context, respect the token budget, and curate ruthlessly. Lowin emphasizes that agents fail when given too many tools or complex arguments, citing a hard limit of 50 tools per agent for reliable performance. He shares real-world examples, including a company with 800 endpoints that could not be effectively exposed, and critiques client implementations like Claude Desktop for not respecting spec-compliant features. The central takeaway: MCP servers should be designed as user interfaces for agents, not as developer APIs.

Don't Build Agents, Build Skills Instead – Barry Zhang & Mahesh Murag, Anthropic

Dec 8, 2025 · 16:22

Barry Zhang and Mahesh Murag of Anthropic argue that instead of building domain-specific agents, developers should build reusable Skills—organized folders of files that package procedural knowledge for agents. They explain that skills are progressively disclosed to protect the context window, use scripts as self-documenting tools, and have already grown to thousands in five weeks, including foundational, partner, and enterprise skills. Skills complement MCP servers by providing expertise while MCP handles connectivity. The future includes treating skills like software with testing and versioning, and enabling agents to create their own skills for continuous learning, ultimately creating a collective knowledge base that makes agents more capable and reliable.

Katelyn Lesse – Evolving Claude APIs for Agents, Anthropic

Dec 4, 2025 · 13:25

Katelyn Lesse, who leads the Claude Developer Platform team at Anthropic, explains how the platform is evolving to help developers build powerful agentic systems using Claude. She details three key areas: harnessing Claude's capabilities through features like extended thinking and tool use, managing context with MCP, a memory tool, and context editing (which combined to yield a 39% performance bump), and giving Claude a computer via the code execution tool and agent skills. The talk also covers challenges like container orchestration for Claude Code on web and mobile, and the importance of letting Claude work autonomously in a sandbox environment.

No Vibes Allowed: Solving Hard Problems in Complex Codebases – Dex Horthy, HumanLayer

Dec 2, 2025 · 20:31

Dex Horthy of HumanLayer argues that with deliberate context engineering — specifically a technique he calls "frequent intentional compaction" — today's AI coding agents can handle large brownfield codebases, not just greenfield projects. He presents a three-phase workflow (research, plan, implement) that keeps agents in the "smart zone" of the context window, avoiding the diminishing returns that set in around the 40% mark. Horthy demonstrates this approach solving a real problem in a 300k-line Rust codebase, shipping a week's worth of work in seven hours with code that passed expert review. He cautions against outsourcing thinking to AI, emphasising that plans must include actual code snippets for reliable execution and team alignment. The talk also addresses the cultural rift where senior engineers clean up slop from juniors' AI tools, calling for top-down adoption and team-wide workflow adaptation.

Designing AI-Intensive Applications - swyx

Aug 9, 2025 · 13:02

In this conference talk, swyx (host) proposes the search for a 'Standard Model' in AI engineering, analogous to physics' Standard Model, to guide practitioners. He examines candidate models: LMOS (LLM Operating System), LMSDLC (AI Software Development Lifecycle), and Anthropic's 'building effective agents' framework. Swyx introduces his own SPADE model (Sync, Plan, Analyze, Deliver, Evaluate), derived from building AI News, a daily tool that scrapes Discord, Reddit, and Twitter with thousands of LLM calls. He argues that the ratio of human input to AI output—ranging from 1:1 in ChatGPT to 0:n in ambient agents—is more useful than debating 'workflow versus agent.' The talk emphasizes that early SDLC stages (LLMs, monitoring, RAG) are becoming commodity, while real value comes from evals, security orchestration, and hard engineering. Swyx encourages attendees to identify and refine their own Standard Models to build products people want.

Building Agents at Cloud Scale — Antje Barth, AWS

Aug 2, 2025 · 19:00

AWS Principal Developer Advocate Antje Barth demonstrates how to build and scale AI agents using cloud-native patterns, arguing that specialized agents will reinvent customer experiences. She showcases Alexa Plus, which orchestrates hundreds of expert systems across 600M+ devices and tens of thousands of services, and the Amazon Q Developer CLI agent, shipped in just three weeks. Barth introduces Strands Agents, an open-source Python SDK for building production-ready agents that supports multiple model providers (Claude, Llama, OpenAI) and over 20 prebuilt tools including memory, RAG, and multi-agent workflows. She demonstrates integrating MCP servers via Lambda with DynamoDB for session storage, and previews upcoming A2A protocol support and a future of personal agents connecting to agent stores.

Ship Agents that Ship: A Hands-On Workshop - Kyle Penfound, Jeremy Adams, Dagger

Jul 27, 2025 · 1:21:00

Kyle Penfound and Jeremy Adams from Dagger demonstrated building production-minded AI agents using Dagger, a programmable delivery engine that runs containerized workflows identically on laptops and in CI. They created a workspace submodule with tools — read file, write file, list files, and run tests — and wired it to an LLM (Claude 3.5) so the agent can edit code and validate changes through the project's actual test suite. The agent runs in isolated containers for determinism and traceability, with behavior visualized via Dagger Cloud. They extended the agent to respond to GitHub issues: a label triggers GitHub Actions to run Dagger, read the issue, hand the assignment to the agent, and open a pull request. The talk emphasized that Dagger's modularity lets developers give agents precisely scoped tools while reusing existing CI workflows, providing guardrails without sacrificing flexibility.

A2A & MCP Workshop: Automating Business Processes with LLMs — Damien Murphy, Bench

Jul 26, 2025 · 1:23:14

Damien Murphy presents A2A and MCP protocols for building multi-agent systems that automate business processes from webhooks, using a host agent that delegates tasks to sub-agents (Slack, GitHub, Bench) via A2A, with each sub-agent using MCP tools from Zapier or internal APIs. He demonstrates processing a meeting transcript to create a GitHub issue, send a Slack message, and research attendees, emphasizing that A2A handles remote agent discovery and opaqueness while MCP provides standardized third-party tool access. Murphy highlights benefits like context isolation—sub-agents absorb large tool outputs and keep the host's context small—and parallel processing, but notes limitations: A2A's early stage, MCP's silent failures (e.g., Zapier Slack missing channels), the non-determinism of LLM orchestration, and the challenge of prompt caching costs. He argues that A2A is best for third-party agents where complexity is hidden, while MCP is useful for extensible tool integration, but if you control the tools or agents, direct function calls are simpler and more reliable.

Ship Production Software in Minutes, Not Months — Eno Reyes, Factory

Jul 25, 2025 · 16:06

Eno Reyes, cofounder and CTO of Factory, argues that AI agents can orchestrate the entire software development lifecycle, moving beyond vibe coding to agent-native development where enterprises delegate planning, coding, testing, and incident response to autonomous droids. He explains that AI tools are only as good as the context they receive—missing context from meetings, whiteboards, or Slack is the primary cause of failure, not LLM quality. Factory's droids search codebases, leverage organizational memory, and question unclear tasks before executing, from generating PRDs and tickets to creating runbooks and RCAs from sentry alerts. Reyes demonstrates how an agent can convert user transcripts and ad-hoc notes into a full feature plan, then break it into parallel tickets for multiple code droids. For incident response, droids pull logs, historical runbooks, and team discussions to produce mitigation plans in minutes, cutting response times in half and shifting from reactive to predictive operations. He emphasizes that the future belongs to engineers who manage agents—thinking clearly and communicating effectively—rather than those writing every line of code.

Machines of Buying and Selling Grace - Adam Behrens, New Generation

Jul 23, 2025 · 19:37

Adam Behrens, CEO of New Gen, argues that AI will transform commerce from static websites to agentic interactions where buyer and seller agents negotiate via intent infrastructure. He traces the evolution from clerk-assisted stores to e-commerce, now to AI-natives where ChatGPT and Claude act as shopping interfaces. Behrens details three challenges: payment delegation (solved via Viza's delegated authentication), product discovery (a unified API akin to Plaid for merchants), and preference representation (two-sided, dynamic, with market-design solutions). He cites Samsung's adoption of an MCP server for chat clients and notes that AI-sourced users convert at higher rates. Behrens predicts revenue sharing via affiliate models and that agents may bypass credit cards for stablecoins, while merchants retain control by embedding transportable data into model providers' surfaces.

How to Build Planning Agents without losing control - Yogendra Miraje, Factset

Jul 23, 2025 · 15:58

Yogendra Miraje from FactSet explains how to build controllable planning agents using agentic workflows and planning by subgoal division, arguing they balance reliability and flexibility for enterprises. He distinguishes workflow agents (static) from agentic workflows (dynamic), and introduces a blueprint generator that pre-selects tools and provides high-level natural language plans to reduce planner cognitive load. He details an architecture using LangGraph nodes (blueprint generator, planner, executor, joiner) and emphasizes designing tools from an agent's perspective with clear purpose, description, and contracts. Miraje stresses evals using code-based and LLM-as-judge techniques, and advises against agentic workflows for fixed tasks, strict compliance, or low-latency needs. The talk includes a concrete example of preparing for NVIDIA's earnings call, showing how blueprints enable structured responses and interpretability.

Building Agents (the hard parts!) - Rita Kozlov, Cloudflare

Jul 23, 2025 · 21:12

Rita Kozlov, VP of Product for Cloudflare's developer platform, presents the building blocks of AI agents—client, AI reasoning, workflows, and tools—arguing that effective agents require all four components. She highlights the Model Context Protocol (MCP) as a standard for exposing APIs to LLMs, and demonstrates Cloudflare's Agents SDK, which simplifies hosting remote MCP servers with built-in OAuth, state management via durable objects, and real-time WebSocket communication. Kozlov cites real-world impact: companies using agents for sales automation see 20% revenue increases, 90% faster support response times, and 50–75% time savings. She walks through a human-in-the-loop credit card approval workflow built with Nok, showing how durable objects maintain long-running state, prevent duplicate actions, and route approvals across Slack, email, or in-app notifications. The talk emphasizes that once an MCP server is deployed, it can be used directly from Cursor, Claude, ChatGPT, or a custom client, including voice interfaces via WebRTC-to-WebSocket translation.

Stateful environments for vertical agents — Josh Purtell, Synth Labs

Jul 22, 2025 · 6:51

Josh Purtell of Synth Labs argues that stateful environments—containerized, network-bounded workspaces that capture external state—make it easier to build effective agents for vertical applications like finance and health. By keeping the application logic separate from the agent, developers can revamp their agent when new models come out without rewriting everything. The environment exposes a tailored representation (e.g., just the terminal, not the whole OS) and supports resets and rollbacks, enabling techniques like tree search that improve long-horizon tasks. Purtell also notes that network boundaries allow reliable multi-agent setups and asynchronous work. The concepts are implemented in the open-source Synth AI Environments repository.

Design like Karpathy is watching — Zeke Sikelianos, Replicate

Jul 19, 2025 · 19:26

Zeke Sikelianos of Replicate analyzes Andrej Karpathy's experience building and deploying MenuGen, a vibe-coded web app that turns menu photos into images, to argue that LLMs are now the primary audience for developer tools. He details Replicate's specific failures—rate limiting and outdated docs that blocked Karpathy—and the fixes they implemented: adding LLMs.txt files for Markdown-friendly documentation, promoting curl commands as the LLM-friendly interface, and launching an MCP server built on OpenAPI schemas. Sikelianos advocates for 'boring technology' like SQL and 'good API hygiene' to keep payloads small and information-dense for LLM context windows. He also calls for better payment acceptance and documentation discipline, stressing that unblocking power users like Karpathy (whose CEO intervened) should not require a viral blog post.

Real world MCPs in GitHub Copilot Agent Mode — Jon Peck, Microsoft

Jul 19, 2025 · 14:27

Jon Peck from Microsoft demonstrates how Model Context Protocol (MCP) servers enable GitHub Copilot Agent Mode to solve real-world engineering problems beyond vibe-coding. He shows a README-driven workflow where Agent Mode builds a full app from a specification, then adds MCPs for database access and GitHub operations. Specifically, he configures a PostgreSQL MCP to pull live schema and data into mock JSON for testing, and the GitHub MCP to commit changes and create pull requests automatically. He emphasizes the read-only safety of the Postgres MCP and recommends using Copilot instructions to enforce practices like change logs. The episode covers how MCPs extend Copilot's capabilities to interact with data sources, testing tools, and DevOps pipelines securely.

The rise of the agentic economy on the shoulders of MCP — Jan Curn, Apify

Jul 18, 2025 · 18:08

Jan Curn, founder of Apify, argues that MCP (Model Context Protocol) enables a future agentic economy where AI agents autonomously discover and purchase tools from other agents or businesses (B2A/A2A). He explains that Apify's marketplace of 5,000 Actors (Docker-based tools) now integrates with MCP, allowing agents to dynamically discover and call any Actor via tool discovery — a key MCP feature. Curn demonstrates this with Claude Desktop, where an agent uses Apify's MCP server to find a venue, scrape Twitter, and even fill a form via a nested MCP server from Browserbase, all without prior configuration. He notes that Apify pays creators over $250,000 monthly, with total Actor revenue exceeding $1.5M/month, and that any developer can publish an Actor to monetize their tools instantly across the ecosystem. The talk closes with open questions about reliability, trust, and whether autonomous agent interaction can lead to AGI.

MCP is all you need — Samuel Colvin, Pydantic

Jul 18, 2025 · 15:24

Samuel Colvin, creator of Pydantic, argues that MCP (Model Context Protocol) is all you need for agent-to-agent communication, simplifying what many are overcomplicating. He explains that MCP's tool calling primitive offers advantages over OpenAPI, such as dynamic tools, logging, and sampling—a mechanism for MCP servers to request LLM calls through the client, enabling nested agentic workflows. Colvin demonstrates a research agent using Pydantic AI that queries the BigQuery public PyPI dataset via an MCP server over stdio, showing how sampling allows the server to generate SQL itself while keeping the main agent's context lean. The demo logs progress to Logfire, an observability platform, and outputs human-readable results. He emphasizes that MCP’s standard I/O mode and extensibility make it ideal for autonomous agents beyond its original desktop coding use case.

[Workshop] AI Pipelines and Agents in Pure TypeScript with Mastra.ai — Nick Nisi, Zack Proser

Jul 12, 2025 · 1:51:14

This hands-on workshop introduces Mastra.ai, a TypeScript framework for building production AI agents and pipelines, and demonstrates how to build an AI meme generator using composable workflows, tools, and agents. Hosts Nick Nisi and Zack Proser walk through creating a multi-step workflow: extract user frustration, find a base meme via ImageFlip, generate captions, and publish the meme at a stable URL. They show how to chain steps with Zod schema validation for deterministic output, then wrap the workflow in an agent that accepts natural language requests. The workshop also covers MCP (Model Context Protocol) and a live demo of MCP.shop for ordering a shirt, emphasizing local iteration with Mastra's playground, built-in memory, and evaluation tools. Real-world patterns include building internal AI assistants for data cleaning, email drafting, and document summarization with minimal code.

Training Agentic Reasoners — Will Brown, Prime Intellect

Jul 7, 2025 · 19:17

Will Brown of Prime Intellect argues that reasoning and agents are fundamentally the same, and reinforcement learning (RL) is the key to advancing both. He explains that RL now works at scale, as shown by DeepSeek's GRPO and OpenAI's o3, and that agentic tasks like tool calling are natural RL environments. Brown warns against reward hacking and emphasizes designing evals that are harder to game than the task itself. He introduces his open-source toolkit 'verifiers' (now on pip) which lets users build trainable agent loops with a simple API, and demonstrates training a 7B Wordle agent in a few turns on just a couple GPUs.

Agents, Access, and the Future of Machine Identity — Nick Nisi (WorkOS) + Lizzie Siegle (Cloudflare)

Jun 30, 2025 · 14:17

Lizzie Siegle (Cloudflare) and Nick Nisi (WorkOS) argue that AI agents need the same authentication and authorization patterns as humans, extending OAuth to machines. They demonstrate an MCP server built with Cloudflare Workers and WorkOS that allows Claude to order a shirt on behalf of a user, showing how agents can act with user credentials. They explain using Cloudflare durable objects for per-user persistent storage and how authorization can be added to MCP servers to control agent actions, including an example where a 'pretty please' tool bypasses a block. The talk emphasizes the need for fine-grained authorization and audit trails as agents scale to thousands of tasks.

Containing Agent Chaos — Solomon Hykes, Dagger

Jun 28, 2025 · 23:48

Solomon Hykes, creator of Docker and founder of Dagger, argues that containing agent chaos requires engineering reproducible execution workflows built on containerization applied to each step of an agent's workflow. He introduces 'container use'—agents developing inside fully isolated, customizable environments rather than just sandboxing outputs—and demonstrates a prototype using MCP to integrate with Claude Code and Goose. The system provides background work, rails, seamless human stepping in, and optionality by leveraging Dagger, Git-based state management, and ephemeral containers snapshotted per action. Hykes shows how agents can run parallel experiments, merge snapshots, and discard failed environments without pollution. The episode concludes with him open-sourcing the project as github.com/dagger/containeruse.

Introducing Strands Agents, an Open Source AI Agents SDK — Suman Debnath, AWS

Jun 27, 2025 · 14:26

Suman Debnath, a Principal ML Advocate at AWS, introduces Strands Agents, an open-source SDK that simplifies AI agent creation by requiring only a model and tools, eliminating scaffolding. He demonstrates building an agent in a few lines of code to read, summarize, and speak a file, using default tools. Another demo integrates Strands with an MCP server to generate animated math videos via Manim, requiring no system prompts—the model reasons autonomously. Custom tools can be created by decorating functions. Strands supports any model via LiteLLM or Bedrock, and is available at strandsagent.com with a GitHub repository for contributions.

The Agent Awakens: Collaborative Development with Copilot - Christopher Harrison, GitHub

Jun 27, 2025 · 1:04:06

GitHub Enterprise Advocates Christopher Harrison and John Peck demonstrate GitHub Copilot's Coding Agent at the AI Engineer World's Fair, arguing that context is the key to effective AI pair programming. They explain that coding agent works by assigning GitHub issues with detailed requirements, then running inside a secure, ephemeral GitHub Actions environment with no internet access and limited write permissions. Harrison emphasizes using `copilot-instructions.md` and `.instructions` files to guide code generation, and MCP servers to access external data or APIs. He stresses that AI does not change the DevOps flow—code still requires manual review, linters, security scans, and unit tests. The session covers agent mode, edit mode, and multi-file edits, along with practical Q&A on enterprise adoption, model transparency, and managing multiple coding agents.

The Eyes Are The (Context) Window to The Soul: How Windsurf Gets to Know You — Sam Fertig, Windsurf

Jun 27, 2025 · 13:37

Sam Fertig, Deployed Engineer at Windsurf, explains how the AI coding IDE achieves its uncanny ability to predict developer intent by focusing on relevant context rather than merely expanding context windows. He breaks context into two pillars: heuristics (user behavior like cursor position, open files, clipboard) and hard evidence (codebase, rules, memories). The core challenge is generating code that fits existing codebases, adheres to personal and organizational standards, and remains future-proof. Windsurf optimizes retrieval quality over quantity, using techniques like embedding search, reranking, and AST parsing to balance latency and scale. Fertig also addresses privacy, stating that while code is sent to servers, data is not stored or used for training.

(possible dupe but better sound) What does Enterprise Ready MCP mean? — Tobin South, WorkOS

Jun 27, 2025 · 13:53

Tobin South from WorkOS explains what it takes to make Model Context Protocol (MCP) enterprise-ready, covering the full journey from a hacky local server to a production system with authentication, authorization, SSO, audit logs, and data loss prevention. He demonstrates buying a shirt via MCP live, highlighting the need for bot blocking, input validation, and adapting auth stacks for dynamic client registration. South also discusses open challenges like headless auth for remote asynchronous workloads and communicating scope between AI agents, emphasizing that authorization is the hardest part of scaling MCP to the enterprise.

Case Study + Deep Dive: Telemedicine Support Agents with LangGraph/MCP - Dan Mason

Jun 22, 2025 · 1:56:13

Dan Mason of Stride presents a case study on building autonomous agents for telemedicine support, replacing a human-driven button-pushing workflow with LangGraph, Claude, MCP, and a Node.js/React/MongoDB stack, achieving roughly 10X capacity increase. The LLM-driven virtual operations associate ("Ava") assesses patient messages, updates state with anchors and scheduled messages, and passes proposals to an evaluator agent that scores confidence and complexity, escalating to humans below 75% confidence. Mason explains how treatment "blueprints" in Google Docs are read directly by the LLM instead of using RAG, enabling new treatments to be added without writing code. He details the eval system using LLM-as-a-judge via PromptFu and retry logic for tool-calling errors, and discusses trade-offs around confidence scoring, prompt caching, and model selection (Claude for steerability). The team includes two software engineers, one designer, and Mason, who wrote most LangGraph code with AI assistance (Kline).

Building Protected MCP Servers — Den Delimarsky and Julia Kasper, MCP Steering Committee & Microsoft

Jun 20, 2025 · 20:13

Den Delimarsky and Julia Kasper, Microsoft engineers on the MCP Steering Committee, explain how the new MCP authorization spec simplifies securing remote MCP servers by separating the resource server from the authorization server, eliminating the need for developers to build custom token factories. They argue that developers shouldn't need to be security experts, so the spec offloads token minting and lifecycle management to standard identity providers like Entra ID, Okta, or Auth0. The client now handles the full OAuth 2.0 dance, while the server only validates tokens via a Protected Resource Metadata (PRM) document. A live C# SDK demo shows a server configured with minimal code, and an Azure API Management deployment demonstrates production-ready remote MCP servers with OAuth proxy and consent flows, including integration with VS Code and GitHub Copilot. The session also previews transforming REST APIs into MCP tools via Azure API Management.

Remote MCPs: What we learned from shipping — John Welsh, Anthropic

Jun 19, 2025 · 15:12

John Welsh, a software engineer at Anthropic, explains how his team standardized on the Model Context Protocol (MCP) for all internal and external integrations, treating it as a universal JSON-RPC stream that can be piped through any transport—including web sockets, gRPC, or even email. He argues that adopting MCP internally solves integration chaos by providing a single, well-defined interface for tool calling, resource access, and authentication, and that building an "MCP gateway" as shared infrastructure creates a pit of success where engineers naturally follow the correct pattern. Welsh details how the gateway handles OAuth flows, credential portability for batch jobs, rate limiting, and observability, while also enabling future features like sampling and prompt injection defense at no extra cost. The talk emphasizes that MCP's message format, not the transport layer, delivers the most value, and that standardizing on any protocol—preferably MCP—reduces engineering overhead and allows teams to focus on high-leverage work.

MCP: Origins and Requests For Startups — Theodora Chu, Model Context Protocol PM, Anthropic

Jun 18, 2025 · 17:45

Theodora Chu, product manager at Anthropic, explains the origin of the Model Context Protocol (MCP) as an open-source standard for giving LLMs agency to interact with external tools and data, born from two engineers copying context manually. She details MCP's evolution from an internal hack week project to wide adoption by Google, Microsoft, OpenAI, and coding tools like Cursor and VS Code. Key protocol decisions include optimizing for server simplicity over client complexity, adding streamable HTTP for bidirectionality, and fixing authentication via community contributions. Chu outlines three startup opportunities: building high-quality MCP servers for verticals beyond dev tools (80% weight), simplifying server building with hosting/testing/deployment tooling, and creating AI security and observability tools. She emphasizes that models are a third user of servers, so tool design must consider end users, client developers, and the model itself.

Beyond Conversation: Why Documents Transform Natural Language into Code - Filip Kozera

Jun 10, 2025 · 10:57

Filip Kozera argues that chat-based interfaces like ChatGPT create ephemeral, polluted context windows and lack forced clarity, making them poor for specifying complex systems, while documents naturally force rigor and structured thinking. He advocates shifting from conversational brainstorming to document-driven workflows, where humans precisely articulate intent for AI agents. Kozera introduces background agents that run asynchronously, triggered by events like emails or meetings, and surface only when needing human approval, turning the human role into one of 'swiping left or right' to approve or edit outputs. He envisions a future where humans manage swarms of such agents, first adopted by prosumers and later by enterprises, with taste and intent becoming critical for imbuing personal brand into agent outputs. The episode presents a clear progression from chat-based brainstorming to structured document-driven agent systems.

MCPs are Boring (or: Why we are losing the Sparkle of LLMs) - Manuel Odendahl

Jun 10, 2025 · 28:32

Manuel Odendahl argues that MCPs (Model Context Protocols) are boring because they constrain LLMs to rigid, predefined tool calls, while LLMs truly shine as code generators that can create dynamic tools on the fly. He critiques the inefficiency of tool calling—repeating context, wasting tokens, and struggling with many tools—and advocates for an 'Eval' approach where the LLM writes and executes code (e.g., SQL, JavaScript) to solve tasks directly. Demonstrating with a JavaScript sandbox, he shows how a single Eval tool can introspect databases, write queries, create REST APIs, and even build a full CRM interface in one call, saving time and tokens. Odendahl urges engineers to think recursively: ask the LLM to write code that writes more code, unlocking infinite loops of creation and restoring the sparkle of LLMs beyond boring function calls.

Are MCPs Overhyped? A Rant about MCPs — Henry Mao, Smithery

Jun 3, 2025 · 7:29

Henry Mao, founder of Smithery and MCP steering committee member, argues that despite MCPs standardizing AI agent-service connections, the ecosystem faces fragmentation, high-friction installation, security, and monetization problems. Users struggle with unreliable MCP servers and complex five-step installs, while developers face hosting challenges, lacking tooling, distribution hurdles, and unclear monetization. Smithery aims to solve these as an AI gateway, demoing an agent that finds GitHub issues and creates Linear tickets using curated MCPs. Mao envisions a future dominated by tool calls where agent experience trumps user experience.

Blender MCP and The Future Of Creative Tools - Siddharth Ahuja

Jun 3, 2025 · 16:50

Siddharth Ahuja introduces Blender MCP, an open-source project that lets LLMs like Claude control Blender via the Model Context Protocol, dramatically lowering the barrier to 3D creation. The tool, which has garnered 11,500 GitHub stars and over 160,000 downloads, enables users to generate complex scenes—like a dragon guarding a pot of gold or a full terrain with nodes—in minutes through natural language prompts. Ahuja explains that Blender’s scripting capabilities are key, and notes that keeping MCP tools lean avoids confusion for the LLM. He demonstrates integrations with AI asset generators and even built an Ableton MCP to create soundtracks, arguing that MCPs will become the glue for creative toolchains, allowing LLMs to orchestrate Blender, Unity, and Ableton together. This shifts creators from tool experts to orchestra conductors, unlocking a new wave of accessible content creation.

MCP Agent Fine tuning Workshop - Ronan McGovern

Jun 3, 2025 · 35:30

Ronan McGovern demonstrates how to fine-tune a Qwen3 model on high-quality reasoning traces from an MCP agent with browser tools. Using Playwright's 25 tools via Model Context Protocol, he generates multi-turn traces by running a 30B Qwen agent on RunPod, saving both tool calls and reasoning content. Those traces are then used to supervised fine-tune a 4B Qwen model with Unsloth, applying LoRA adapters to attention and MLP layers. The process includes converting MCP tool schemas to OpenAI format, extracting Hermes-style tool calls, and unrolling conversations to multiply training examples. Even with only nine curated traces, the fine-tuned model shows improved tool-calling behavior on multi-step tasks like navigating trellis.com to extract specific content.

Will Agent evaluation via MCP Stabilize Agent Networks? - Ari Heljakka

Jun 3, 2025 · 14:11

Ari Heljakka, CEO of Root Signals, argues that agent evaluation via the Model Context Protocol (MCP) can stabilize agent networks by enabling self-improvement loops. Using a hotel reservation agent example, he shows that without MCP the agent wrongly recommended a competitor hotel, but with MCP it invoked policy evaluators and corrected its behavior automatically. He also demonstrates marketing message optimization via Cursor, where the agent scored and improved text using an MCP-connected evaluation charge. Heljakka emphasizes that success requires a powerful evaluation library with systematic lifecycle maintenance, and introduces Root Signals' free MCP server. The approach promises more controllable, transparent, and dynamically self-correcting agents across different frameworks.

From PM at Stripe to Building an AI startup, a recent founder's journey - Mounir Mouawad

Jun 3, 2025 · 11:59

Mounir Mouawad, CEO and co-founder of Porsche AI, explains how building an AI startup differs from product roles at Stripe, Google, and Amazon, using video game analogies. He argues user problems are an 'emergent property' requiring hypothesis-driven iteration rather than conventional roadmaps. Product development is gratifying with releases in hours or days, but velocity is a 'stable stick' as opportunities like MCP come and go quickly. The hardest part is outreach without big brand support—like playing Crash Bandicoot without boosters—so he finds people followers, advocates, and partnerships (e.g., with Browserbase) essential. He asks listeners to star Porsche AI's GitHub repo.

Real AI Agents Need Planning, Not Just Prompting - Yuval Belfer

Jun 3, 2025 · 7:58

Yuval Belfer of AI21 Labs argues that LLMs alone still fail at instruction following, as shown by GPT-4.1's struggles in 2025, and that true AI agents require dynamic planning, not just prompting. He critiques ReAct for lacking look-ahead, contrasting it with AI21 Maestro's planner and smart execution engine, which uses best-of-n sampling, candidate pruning, and replanning. On AIfeval, Maestro pushes GPT-4.0, Claude Sonnet 3.5, and R3 Mini to near-perfect scores; on internal requirement satisfaction benchmarks, it improves over single LLM calls despite higher runtime and cost. Belfer advises starting simple (SLMs, ReAct) and escalating to planning only for complex tasks, inviting listeners to join the Maestro waitlist.

Invisible Users, Invisible Interfaces: Accelerating Design Iteration with AI Simulation - Alex Liss

Jun 3, 2025 · 12:37

Alex Liss, VP of Data Science and AI at Huge, argues that AI should be used as a tool for design itself rather than as a feature, proposing Intelligent Twins—AI simulations of user personas—to accelerate needfinding and interface testing. Citing a trust gap where only 32% of US adults trust AI, he advocates for repairing it by designing better interfaces, not adding chatbots. He demonstrates a global audit of sports websites (NBA, Olympics, Premier League) where Intelligent Twins simulated casual and super fans across 72 tasks, revealing strong navigation but declining performance in information architecture and engagement. The methodology uses computer use agents for visual evaluation and human-in-the-loop review to generate focused design briefs. Limitations include reproducibility and the need for test-and-control validation, with future potential in integrating MCP protocol for turning Figma components into code. Liss concludes that AI simulation can help teams gather insights faster and create simpler, trustworthy interfaces.

Letting AI Interface with your App with MCP — Kent C Dodds

Jun 3, 2025 · 19:54

Kent C Dodds explains how Model Context Protocol (MCP) enables AI assistants like Claude Desktop to interface with any app or service, moving beyond limited built-in integrations. He traces the evolution from early LLM chat (phase one) through host applications with custom integrations (phase two) to MCP's open standard (phase three), arguing that MCP lets developers build once for any assistant. In his live demo, he runs a Locationator server to derive his location, a GetWeather server for weather, and an Epic Me server with OAuth authentication to create a journal entry. MCP servers handle tools, resources, and prompts; the client standardizes communication while the server controls unique logic. Kent contends this protocol is how we give AI "hands" to perform real tasks, leading toward a universal Jarvis-like assistant that can dynamically connect to any service without manual integration per platform.

The Devops Engineer Who Never Sleeps — Diamond Bishop, Datadog

Apr 22, 2025 · 16:18

Diamond Bishop, Director of AI Engineering at Datadog, explains how his team builds AI agents—the On-Call Engineer and Software Engineer—that automate DevOps tasks like incident investigation, remediation, and postmortem writing. He details how the On-Call Engineer wakes up for alerts, reads runbooks, queries logs and metrics, and suggests fixes to let human engineers sleep. The Software Engineer proactively fixes errors by generating code diffs and pull requests. Bishop shares four key lessons: scope tasks and evaluate rigorously, assemble teams of optimistic generalists and UX experts, adapt UX for human-agent collaboration, and treat observability as critical for debugging multi-step agent workflows. He predicts that within five years, AI agents will surpass humans as primary users of SaaS platforms like Datadog, urging builders to design for agent consumers.

How We Build Effective Agents: Barry Zhang, Anthropic

Apr 4, 2025 · 15:09

Barry Zhang of Anthropic's Applied AI team argues that effective agents require simplicity, not complexity, and should be built only for tasks with high value and ambiguous problem spaces. He offers a checklist: ensure task complexity is high, value justifies token cost, critical capabilities are de-risked, and errors are easily discovered (e.g., coding with unit tests). Agents are just models using tools in a loop — environment, tools, and system prompt — and he advises iterating on these three components before optimizing. To improve agents, developers should think like them by narrowing their perspective to the agent's 10-20k token context window and even asking Claude to critique its own tools and trajectories. Zhang forecasts three open challenges: making agents budget-aware by enforcing time/token/money limits, enabling self-evolving tools via meta-tools, and building asynchronous multi-agent communication beyond synchronous turns.

Why Agent Engineering — swyx

Mar 24, 2025 · 11:45

Swyx argues that 2025 is the year of agent engineering, pivoting the AI Engineer Summit to focus exclusively on agents and explaining why agents are now viable due to advancements in reasoning, tool use, model diversity, and a 1000x cost reduction in GPT-4-level intelligence over 18 months. He highlights that OpenAI's ChatGPT grew 33% to 400M users in three months after shipping agentic O1 models, and projects it will reach 1B users by year-end. Swyx cites Simon Willison's crowdsourced definitions and OpenAI's new agent definition, while calling for a halt to demos of flight booking agents and Reddit astroturfing. He frames agent engineering as the evolution of AI engineering, distinct from MLE and software engineering, with practical use cases like coding and support agents having clear product-market fit.

How Windsurf writes 90% of your code with an Agentic IDE - Kevin Hou, Windsurf

Mar 11, 2025 · 20:50

Kevin Hou, head of product engineering at Codeium, introduces Windsurf as the first AI agent-powered editor, arguing that agents are the future of software development. The editor is built on three principles: trajectories, a unified timeline that lets the agent read the user's mind and execute commands like 'continue my work'; meta-learning, which auto-generates memories of user preferences and codebase context; and scaling with intelligence, removing legacy features like chat and @-mentions as models improve. Windsurf launched three months ago and has already generated 4.5 billion lines of code, with 90% of code now written via its Cascade agent. Hou emphasizes that the agent reduces human-in-the-loop effort by inferring context, running terminal commands in the user's environment, and dynamically retrieving documentation—allowing developers to contribute less input while getting more production-ready output.

Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic

Mar 1, 2025 · 1:44:12

Mahesh Murag of Anthropic presents the Model Context Protocol (MCP) as an open standard that replaces fragmented integrations with a single protocol for connecting AI systems to data sources, enabling context-rich AI applications and agentic experiences. He explains MCP's philosophy, inspired by APIs and LSP, and its three interfaces: tools (model-controlled), resources (application-controlled), and prompts (user-controlled). Murag highlights adoption with over 1,100 community-built servers and official integrations from companies like Cloudflare and Stripe. He demonstrates building agents with MCP using the MCP-Agent framework, showing how agents can use tools dynamically and composably across hierarchical systems. Future plans include remote server support with OAuth 2.0, a centralized registry for discovery and verification, and enabling agents to self-evolve by dynamically finding new capabilities via registry search.

Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks

Feb 22, 2025 · 25:18

Roy Derks argues that tool calling is the most critical yet overlooked component of AI agents, far more than mere 'plumbing.' He contrasts traditional tool calling—where developers manually manage callbacks, retries, and errors within the agent loop—with embedded tool calling, a black-box approach used by frameworks like LangChain’s createReactAgent. Derks advocates for separation of concerns via the Model Context Protocol (MCP) from Anthropic, which splits tool logic into MCP servers communicating with clients, and via standalone tool platforms such as IBM’s wxflows, Composio, and Toolhouse that let teams build tools once and reuse them across LangChain, CrewAI, or AutoGen. He also introduces dynamic tools, where an agent generates queries on the fly—e.g., using GraphQL or SQL schemas—instead of defining hundreds of static tools, noting that LLMs like Claude handle GraphQL well but may hallucinate on deeply nested schemas. The episode emphasizes that 'an agent is only as good as its tools' and provides practical guidance on designing tool descriptions (which act like system prompts) and output schemas to enable type-safe, chainable tool calls.