Databricks

Thinner Agents on a Smarter Substrate: The Ontology-based Semantic Layer — Emil Eifrem, Neo4j

Jul 22, 2026 · 11:06

Emil Eifrem of Neo4j argues that scaling AI agents at large enterprises requires thin agents on a smarter shared substrate built from three pillars: a business ontology naming real concepts (customers, accounts) in plain language, a technical ontology cataloging all data sources and schemas with a mapping between them, and execution traces recording each agent's attempts and outcomes. These three layers solve four problems: data discovery (teams no longer hunt from scratch), trust (top-down curation and bottom-up success signals), DRY (a single mapping propagates changes across all agents), and cross-agent learning (agents improve over time via traces). The approach is based on work with a Fortune 20 bank, a Bay Area tech platform, and a leading fintech. Eifrem says this enables many more agents without re-engineering each time.

You Didn't Ship a Bug. You Just Wrote It for a Human. - Ravi Madabhushi, Scalekit

Jul 19, 2026 · 12:50

Ravi Madabhushi, co-founder of Scalekit, explains why infrastructure built for humans breaks when AI agents act 60 times faster than users, citing a production database spike caused by a 'last seen' timestamp updating every tool call. He argues that OAuth scopes, designed for deterministic human-written programs, cannot constrain non-deterministic agents, which need attribute-level, time-bound, and principle-scoped permissions. Madabhushi warns that 60% of LLM errors stem from rate limits designed for humans, not agents, and advocates for just-in-time authorization and absolute visibility into every agent action—who took it, on behalf of whom, and when authorized. He concludes that without deterministic guardrails, teams are merely 'praying' agents won't delete production data.

The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

Jun 18, 2026 · 37:06

Sandipan Bhaumik, a technical lead for Data and AI at Databricks, presents a five‑pillar playbook for taking AI agents to production: evaluation (define numerical success before touching code), observability (trace every decision for regulators and debugging), data foundation (agents do not forgive bad data), multi‑agent orchestration patterns (orchestrator‑worker, choreography, human‑in‑the‑loop), and governance (PII pre‑validation, prompt versioning as change management). He recounts a retail bank that spent £85,000 over six months on a chatbot PoC that failed because no one could measure or trace it. His team reversed the order: they built the evaluation dataset and tracing infrastructure first, selected the model in week 7 of an 8‑week engagement, and launched successfully. Six weeks post‑launch, when the bank updated interest rate policies, the tracing system caught that the new document had not been re‑embedded, so the agent served stale answers—a production incident the five pillars were designed to handle. The evaluation dataset is a living system that grows from 200 test cases; a production incident playbook connects all pillars: detect via eval dashboard, diagnose with…

How agent o11y differs from traditional o11y — Phil Hetzel, Braintrust

May 28, 2026 · 20:43

Phil Hetzel of Braintrust argues that agent observability differs fundamentally from traditional observability, which only answers 'is the system up?' Agents are non-deterministic, with individual traces exceeding a gigabyte and spans reaching 20 megabytes, packed with unstructured text that traditional tools like Datadog or Grafana cannot handle. Braintrust built a custom database from scratch—featuring a write-ahead log for instant visibility, analytical indexes for filtering, and a forked Rust-based Tantivy index for full-text search—to ingest and query this data in real time. Unlike traditional observability, agent observability involves non-technical experts (e.g., clinicians, lawyers) who grade traces and write justifications that become training signals for automated scoring functions. Braintrust also runs lightweight LLMs on traces to perform clustering and topic modeling, surfacing user intent, sentiment, and failure modes to accelerate the iteration loop between production issues and fixes. The episode also covers Braintrust's shift from ClickHouse to a custom database due to ClickHouse's limitations in text indexing.

From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik

Apr 8, 2026 · 26:29

Sandipan Bhaumik, Data & AI Tech Lead at Databricks, argues that scaling AI agents from one to five transforms an AI problem into a distributed systems problem, exposing teams to race conditions, stale reads, and cascading failures. He recounts a production deployment where a credit decisioning system incorrectly approved 20% of applicants because a caching layer wasn't invalidated between agents. Bhaumik presents two coordination patterns: choreography (event-driven, decentralized) for simple workflows with high autonomy, and orchestration (central coordinator) for complex workflows needing rollback and observability. He advocates for immutable state snapshots (append-only versions) and data contracts (schema validation at handoffs) to eliminate race conditions, and demonstrates failure recovery using circuit breakers and compensation (saga) patterns. The episode delivers a production-grade architecture on Databricks using LangGraph, Unity Catalog, Delta Lake, and MLflow for tracing, with code examples for state handoff and circuit breaker logic.

2025 is the Year of Evals! Just like 2024, and 2023, and … — John Dickerson, CEO Mozilla AI

Aug 6, 2025 · 19:14

John Dickerson, CEO of Mozilla AI and former co-founder of Arthur AI, argues that 2025 is finally the year for AI evaluation (evals) due to three converging forces: ChatGPT making AI tangible to the C-suite, enterprise budget freezes that funnelled funding into GenAI pet projects, and the rise of agentic systems acting autonomously. He traces how ML monitoring existed pre-2022 but lacked C-suite attention, with Jamie Dimon’s JPMC spending only $100 million on AI from 2017 to 2021. The ChatGPT launch on November 30, 2022, unlocked CEO discretionary budgets for GenAI, leading to 2023 science projects, 2024 production deployments, and 2025 scaling. Now, as agents reason and act, connecting evals to downstream business KPIs—risk mitigation, revenue gains—is a first-class discussion for CEOs, CFOs, CISOs, and CTOs. Dickerson notes that evaluation companies have shifted to multi-agent system monitoring, and while LLM-as-judge is popular, biases require human validation, with companies like Mercor hiring experts at $50–200/hour to lockstep multi-agent outputs.

The Rise of Open Models in the Enterprise — Amir Haghighat, Baseten

Jul 24, 2025 · 16:50

Amir Haghighat, CTO of Baseten, argues that enterprises are increasingly moving from closed frontier models like OpenAI and Anthropic toward open source models, driven by four specific cracks in the assumption that closed models will work indefinitely: quality for specialized tasks (e.g., medical document extraction), latency requirements (especially for voice), unit economics ballooning from agentic use cases where a single user action triggers 50 inference calls, and the desire for competitive differentiation. Drawing on conversations with over 100 enterprises, he explains that while most started with dedicated deployments on Azure/AWS for toying around in 2023, by 2024 about 40-50 had production use cases, and in 2025 the shift accelerated. However, adopting open models forces enterprises to build inference infrastructure, facing challenges like speculative decoding, prefix caching, guaranteeing four-nines reliability with hardware failures and VLM crashes, and scaling replicas—with one Fortune 50 soft drink company reporting an eight-minute spin-up time. Haghighat concludes by contrasting the simple API-call world with the complexities of mission-critical inference, where…

"Data readiness" is a Myth: Reliable AI with an Agentic Semantic Layer — Anushrut Gupta, PromptQL

Jun 27, 2025 · 17:02

Anushrut Gupta of PromptQL argues that 'data readiness' is a myth — perfect, clean data is unattainable — and instead advocates for an agentic semantic layer that learns from user corrections. He contrasts traditional approaches like manual semantic layers and knowledge graphs, which break as business definitions change, with PromptQL's design: a deterministic domain-specific language (PromptQL) that lets an LLM generate a plan executed by a runtime, avoiding hallucination. The system behaves like a new hire analyst: day zero it can handle messy tables (e.g., 'Morc, Plug, Zorp'), and through human guidance it self-improves — learning 47 business terms, mapping six systems, and discovering 12 calculation variants within 30 days. Gupta demonstrates a multi-step query across databases, Zendesk, and Stripe, with explainable steps and editable 'brain'; the AI achieves 100% accuracy on complex tasks for customers like a Fortune 500 food chain and a fintech company.

Trust, but Verify: Knowledge Agents for Finance Workflows - Mike Conover

Apr 9, 2025 · 21:10

Mike Conover, CEO of Brightwave, explains how his company builds knowledge agents for financial workflows, digesting thousands of pages of content for due diligence and research. He argues that non-reasoning models perform only local search, so winning systems must use end-to-end reinforcement learning over tool-use calls to achieve globally optimal outputs. Conover describes design patterns like decomposing research into sub-themes, using secondary calls for error correction, and avoiding the latency trap of long feedback loops. He emphasizes that synthesis—weaving facts across documents—remains a hard problem due to limited recombinative reasoning in training data. Brightwave's product reveals its thought process through interactive citations and structured findings, allowing analysts to drill into any passage. The episode also notes the company is hiring, offering a $10,000 referral bonus.