Journal

If you check a user's permissions on every API call, you're doing auth at the wrong layer. Inject the claim into the JWT at login with a Supabase auth hook, so the token carries it. Here's the pattern, plus the two traps that make it fail silently.

Production RAG over regulations isn't embed-and-retrieve. It's a layered pipeline (hybrid search, reranking, hierarchical summaries, graph context), and the failure that bites hardest is a silent embedding-dimension mismatch that returns confident garbage.

A loose-cannon agent is dangerous and a shackled one is useless. The way out is to put the judgment in versioned, fail-closed skill definitions and to gate which tools the agent can touch per skill and per turn. Capable without being a liability.

The thing that takes down an AI system in production usually isn't the model or the app. It's the boring infrastructure underneath, and it fails green. Four real ones from a dockerized AI stack: a mount writing to the void, a runaway container, a tripped autoscaler, dead proxy routes.

A context window is not memory. For an agent that handles one client's accounting across nine months, we built memory as five distinct layers (facts, history, decisions) with a promotion path that turns a one-off ruling into a standing rule.

Making a real ERP usable by AI agents isn't an integration project. It's a contract. We exposed a fiduciary back office as 13 MCP servers and 222 tools, one server per domain, one uniform envelope, per-principal auth. Here's the shape that holds.

In a regulated business, compliance isn't a layer you add after the AI works. It's the constraint that decides what the architecture is allowed to be. Here's how Swiss data and professional-secrecy law shaped every layer of an AI system we built for a fiduciary.

Most 'we use AI' stories are an autocomplete in someone's editor. Ours is an org chart: a CTO agent, domain leads, a coordination layer, tickets claimed off a board, one isolated worktree per agent, and a review gate nothing skips. Here's how it actually runs.

The reliable parts of a production AI agent aren't in the model. They're in the deterministic code wrapped around it. Stop trying to prompt your way to correctness and start constraining what the model is allowed to do.

If your agent token bill keeps climbing, the model price usually isn't the problem. The waste is in how much context you pay for on every call. The real cost drivers, and the levers that actually move them.

Lines of AI-written code and acceptance rates measure activity, not impact. The honest question is whether your team ships more of the right work at the same quality. How to read that, and the one number that's actually real.

Making an agent reliable is one problem. Governing a non-human actor with commit access is another: who owns its actions, how far a bad one reaches, and whether you can prove what happened. The governance layer, plainly.

The bugs that hurt a production pipeline don't crash. They return 200, paint the dashboard green, and quietly stop doing their job. Here are five we hit running an agentic accounting platform, and how we caught them.

AI readiness isn't a license count. It's whether your team can run agents on real work in production. Six dimensions tell you where you actually stand, and which gap is stalling you.

Most AI consultancies sell decks or dependency. A few transfer real capability onto your team. Six questions that tell the difference before you sign.

Agents lose context because a big repo doesn't fit a window, and a bigger window doesn't fix it. The fix is serving the canonical answer on demand.

Reliable agents aren't a better model, they're the engineering around it: scoped permissions, review gates, observability, and context the agent can trust.