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Field Notes
Guardrails Are a Design Review, Not a Filter
Most teams treat guardrails as a filter on what a model says. The failures that matter happen in what an agent does — which tools it may call, what it may change, and when a person must decide. The layered stack that treats guardrails as a design review, before a line of code ships.
Field Notes
The Eval Set Is the Product Spec
A feature list says what a team hopes an AI system will do. An eval set says what it must do, on which inputs, judged how. Why serious teams write the tests before the features — and how failure taxonomies and regression gates turn opinions about quality into evidence.
Field Notes
Agents Don't Have a Capability Problem. They Have a Permission Problem.
In a single week, OpenAI shelved a flagship model for acting without permission and misreporting its own actions, coding agents leaked 13,000 internal screenshots while doing exactly what they were asked, and the industry's fixes shipped: consent gates and default-deny runtimes. The bottleneck for production agents is no longer what they can do.
Field Notes
RAG Fails at the Edges, Not the Demo
Every RAG demo works — on twenty clean documents and the questions the builder wrote. Production corpora bring duplicates, stale versions, mangled tables, permissions, and real user questions. The six edge failures, what causes each one, and the fix.
Field Notes
Software Doesn't Need Sentences: Jev, System One Models, and the Decision Layer
TypeSafe AI's Jev doesn't generate text at all. It returns typed, probabilistic decisions in milliseconds — and claims it can't hallucinate. We read the launch post, the docs and the evals: what's genuinely new, what the guarantee actually covers, and where a decision layer fits in production AI.
The Deployment Land-Grab: Why Deployment Became AI's Main Event in 2026
In ten weeks in spring 2026, OpenAI, Anthropic, AWS, Microsoft, and Google Cloud announced deployment ventures with headline figures totalling roughly $9–10 billion. The money is not like-for-like — and that difference tells you who wants to own your AI roadmap.
What Is Forward Deployed Engineering? The Playbook Behind AI That Actually Ships
Forward Deployed Engineering puts senior AI engineers inside your company — in your repo, behind your SSO, against your real data — until the system is live in production and your team owns it. What the role actually involves, and how it differs from staffing, consulting, and solutions engineering.
Field Notes
Agent = Model + Harness
The model is the soul of an agent. The harness is the body — it lets the agent understand its environment, use tools, and keep working when the real world pushes back.
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