Ai-Agents

fleet-brain’s CLM harness drove real omp sessions past the local 524K window: promotion fires at 84% of the window and rides the prompt cache; with promotion disabled, a snapcompact compaction cut 445K tokens to 45K while preserving gist and destroying every verbatim canary; the headline surprise — canary recall collapsed between 389K and 443K with no context-management event at all, meaning attention had already failed exactly where the managed boundary sits. Plus: the overflow-withhold mechanism I set out to observe is unreachable on the tool surface, because every tool result is capped upstream.

An audit of my coding agent’s model routing turned into a routing philosophy plus three real bugs. The principle: route by token volume, not vibes — the default loop, subagent fan-out, and background work run on local GPUs for free, while planning, review, and deep reasoning buy cloud quality. Found along the way: a context-promotion target pointing at a provider I have no credential for (a silent no-op), omp’s internal judge role degrading to prompted 27B judging because no TypeSafe key exists (fixed with Ollaya’s winnow:e4b decision model on the senses node, calibrated answers in 0.93 s on CPU), and the DeepSeek V4.1 naming trap where the literal model id does not exist — deepseek-flash IS V4.1-Flash, and deepseek-v4-pro server-routes to it anyway. Plus the GLM-5.3 flash/base vision trap, verified with live image requests.

A tournament report from the AI Chessathon: a pure-Python numba chess engine on one CPU core, four replacement neural evaluators that all lost to classical piece-square tables (one 0-30), the residual NNUE that beat them (+224 Elo, confirmed at tournament time control), a time-management bug found by post-mortem forensics mid-event, and the promotion gates that kept an AI coding agent honest. Final: 58th of 465, rating 2207.

I have been building Cairnkeep since early July: project-scoped memory that survives sessions and crosses harnesses, a retrieval-first protocol agents must follow instead of new autonomy, least-authority MCP surfaces (read-only tool profiles, capability gates, immutable context packs), and optional, explicit opt-ins for everything networked. This post is the what and the why, the production deployment I run it in (a VPS-hosted memory server for a four-machine fleet, with the work laptop deliberately isolated), the 26-lesson learning path with its course labs repo, and the companion video series on my BitsEntangled YouTube channel.