Temporal

The Building Blocks of GTM Orchestration — Arman Vaziri, Ramp

Aug 26, 2026 · 19:55

Arman Vaziri, who leads product and sales led growth engineering at Ramp, details go-to-market orchestration: an intent — offering Pro V1 golf balls to golfers at East Coast construction companies — becomes targeted outbound, paid creative, landing page and in-app nudges. He argues the bottleneck was never ideas but messy data, rep busy work, and coordination cost; Ramp's answer was an internal CDP on Kafka and Postgres, plus embedded unstructured data for search. Pre-meeting briefs for account managers map attendee emails to accounts and run as durable Temporal threads; a skill library for custom brief formats drove adoption. For smaller teams, build narrow automations first — three years ago two people used GPT-3.5 for outbound — because nobody gets a year to design a perfect architecture.

AI in GTM at Notion — Flora Liu

Aug 26, 2026 · 21:15

Flora Liu, an engineer on Notion's GTM team, argues GTM is now a distributed systems problem, not marketing ops, and details how Notion built a unified decisioning system across self-serve and sales-assist. Workflows reduce to four questions — what do we know, what should happen next, how to execute safely, did it work — built as Know, Decide, Act, Learn layers. Snowflake computes the profile, DynamoDB serves it in milliseconds, and Notion is the substrate where humans and agents operate on the same context; signals become Temporal workflows. Agents research and draft, humans approve customer actions, and contact-sales forms are untrusted input. After 13 weeks, enterprise reps log more qualified opportunities, and context-aware recommendations made users 63% more likely to take next step.

Benchmarking Coding Agents on New vs Legacy Codebases — Denys Linkov, Wisedocs

Aug 8, 2026 · 18:08

Denys Linkov of Wisedocs, whose medical-claims ML pipeline ran across ten legacy repos, argues his team's six-month monorepo refactor was worthwhile even as coding agents improve fast. A refactor task that took o3 three hours and ten major mistakes now takes about one-fifth the time: Sonnet 4.6 needed one extra iteration, Opus 4.8 nearly one-shot it. Yet GPT 5.5 extra high 'completed' the job in 10 minutes 22 seconds, writing 2,000 lines of scaffolding with models missing and admitting no deployment or bootstrap command. So Linkov reads METR's task-length curve at 80-90% success, not 50%; an hour-long agent run at coin-flip odds wastes the hour and your attention. The payoff was social too: commit velocity never flattened, months-long features ship in under a week, and developers now volunteer across the monorepo.

Vibe Engineering Effect Apps — Michael Arnaldi, Effectful

May 7, 2026 · 1:43:04

Michael Arnaldi demonstrates that cloning the Effect library's repository into a project, rather than relying on prompts, is the most effective way to give coding agents the context they need to build reliably with Effect. Starting from an empty repo, he sets up a Bun, Vitest, and TypeScript project, adds the Effect repo as a git subtree, and creates an agents.md to guide the agent. He then uses the agent to research patterns, resulting in a fully functional Todo HTTP API with OpenAPI docs, SQLite persistence, and tests — all from scratch in under two hours. The workshop stresses that treating the library code as part of the project, combined with strict diagnostics and pattern files, makes agents effective even in unfamiliar codebases. Arnaldi also discusses the importance of workflow systems like Effect Cluster for long-running AI processes, where server failures become likely.

OpenAI + @Temporalio : Building Durable, Production Ready Agents - Cornelia Davis, Temporal

Jan 12, 2026 · 1:18:30

Cornelia Davis, developer advocate at Temporal, demonstrates how the OpenAI Agents SDK and Temporal combine to build durable, production-ready AI agents. The integration, co-developed by OpenAI and Temporal, wraps agentic loops in Temporal workflows, providing automatic retries, event sourcing, and state management that survive process crashes and network failures. A live demo shows a weather alert agent that calls an LLM, invokes tools like get_weather_alerts and get_ip_address, and recovers seamlessly after the worker is killed. Davis highlights that Temporal’s architecture treats processes as logical entities, allowing agents to run for days and handle human-in-the-loop delays without developer-managed infrastructure. She also explains handoffs and micro-agent orchestration, and points to the AI Cookbook on docs.temporal.io with ready-to-run recipes including the OpenAI Agents SDK integration.

Vision: Zero Bugs — Johann Schleier-Smith, Temporal

Nov 24, 2025 · 36:03

Johann Schleier-Smith, Technical Lead for AI at Temporal, argues that zero-bug software is achievable by applying aerospace-grade techniques like rigorous specification, modular design, and formal verification, now made practical by AI. He cites the Airbus A320's control software (no incidents from software in decades) and the Space Shuttle (1 error per 420,000 lines) as proof. Schleier-Smith demonstrates Dafne, a language that embeds proofs inline with code for automated verification, and shows how agentic coding can reduce high-assurance code costs by ~100× compared to traditional development. He concludes that agents generating verified code will overcome current quality limitations and trigger widespread adoption.

From Stateless Nightmares to Durable Agents — Samuel Colvin, Pydantic

Nov 24, 2025 · 22:13

Samuel Colvin of Pydantic demonstrates building production-grade durable AI agents using PydanticAI, Temporal, and Pydantic Logfire, arguing that stateless architectures fail at scale and that durable execution with checkpointing and recovery is essential. He shows a 20 questions game where two agents play, but with 20% simulated failures, restarts are avoided by wrapping agents in Temporal wrappers for automatic retries. Colvin then implements a Deep Research agent that plans searches, runs parallel web searches via Tavilli, and synthesizes results, all recoverable. In a live demo, killing the workflow and restarting resumes instantly, replaying cached LLM calls in milliseconds. He also previews Pydantic AI Gateway and demonstrates Pydantic Evals comparing Gemini, GPT-4.1, and Claude Sonnet 4.5 on performance and cost.

Events are the Wrong Abstraction for Your AI Agents - Mason Egger, Temporal.io

Jun 27, 2025 · 14:40

Mason Egger, a Senior Developer Advocate at Temporal, argues that event-driven architecture (EDA) is the wrong abstraction for AI agents and proposes durable execution as the correct one. He compares EDA to a geocentric model—complex and error-prone—and durable execution to a heliocentric model that simplifies development by automatically preserving state, retrying failures, and decoupling logic from infrastructure. Egger critiques EDA for lacking clear APIs, scattering business logic, creating ad hoc state machines, and causing race conditions, noting that EDA is only loosely coupled at runtime, not design time. He presents Durable Execution, implemented by Temporal's open-source platform, as a crash-proof approach that virtualizes execution across machines and languages (Go, Python, TypeScript, etc.), letting developers focus on core logic instead of queues and events. The talk concludes that all AI agents are distributed systems, and durable execution abstracts away event complexities, making failures inconsequential.

Exposing Agents as MCP servers with mcp-agent: Sarmad Qadri

Jun 11, 2025 · 18:05

Sarmad Qadri, CEO of Lastmile AI, argues that agents should be exposed as MCP servers, enabling multi-agent composition and async workflows. He introduces mcp-agent, a framework implementing Anthropic's effective agent patterns—augmented LLM, evaluator-optimizer, orchestrator—on top of the Model Context Protocol. Qadri demonstrates an agent that reads a student story, fetches an APA style guide via a fetch MCP server, and writes a graded report, all orchestrated as a durable Temporal workflow. He explains that MCP standardizes tool connections for LLMs, shifting agentic behavior from client-side to server-side, allowing agents to be invoked from any MCP client like Claude or Cursor. The talk covers how this paradigm enables scalable, platform-agnostic agents that can be paused, retried, and scheduled.

How agents broke app-level infrastructure - Evan Boyle

Jun 3, 2025 · 13:32

Evan Boyle, founder of GenSX, argues that LLM-powered agents have broken assumptions about app-level infrastructure, as p1 latency jumps from milliseconds to seconds and workflows run minutes to hours. He explains that traditional serverless providers timeout after 5 minutes, lack native streaming, and force brittle Rube Goldberg machines on top of queues like SQS or tools like Airflow. Boyle presents an open-source library that separates API and compute layers using Redis streams for resumability, enabling users to refresh pages or navigate away without losing progress. He demonstrates components with built-in retries, caching, and tracing, and a workflow that automates Hacker News analysis. Key architectural lessons include starting simple but planning for long-running agents, keeping compute and API planes separate, and leaning on Redis for resumability.