Ten CI gates, and the failure behind each one
Every gate in this repository exists because something got through. Three of them because a suite reported success against a broken artifact.
10 min read
Topic
AI agents in production
Agents whose outputs land in typed, audited tables rather than chat transcripts. LangGraph, CrewAI, Google-ADK, MCP, structured contracts and fallbacks.
I write about the boundary between a useful AI demo and a system another workflow can trust. The central rule is simple: an agent’s output should become a typed, validated, traceable record before it changes a product or a business process. The hard work is rarely a clever prompt. It is tool design, bounded permissions, evaluation, provenance, fallback behavior, and deciding what the system must do when the model is confidently wrong.
3 posts
All writingAgents write the diff. The repo has to review it.
How to run Cursor, Claude Code, and Codex in an existing repo: AGENTS.md, CODEOWNERS, CI gates, and the AI-generated PR pitfalls that blow up review.
15 min read
The data platform is part of the product
A practical operating model for reliable data and AI products: consumer-facing promises, typed decisions, idempotency, provenance, and repair.
10 min read