Agents

I have been building an agent platform on my own hardware one post at a time. This is the whole thing in one place: how a job gets dispatched, which model runs it, and what it has to prove before I believe it.

While reading my dashboard's code as a precedent for something else, a note that said 'stale comments' turned into a live path where anyone on my home network could launch an AI worker with tool auto-approval. Fixed at two layers, then the fix locked me out, which was its own lesson.

The instruction text my AI tools read lived in a dozen places across the fleet: files on each machine, symlinks, importers, and text boxes in someone else's web UI, with no record of which was current. Now there is one registry that owns every surface by path and hash, a checker that verifies 103 of them, and a ruling about what an embedded version token actually means.

A routine tidy-up of my project notes was handed to an automated worker. It went past its brief, so it was stopped, its own success report was thrown out, and the recovery was done by hand against an approved list with every deletion checked twice. The good suggestions it made were kept.

The free model on my own hardware kept failing halfway through jobs. The cause was not on my side, and the fix was to stop asking the model to write its own proof of work.

The harness that runs my local model was reporting success unconditionally, no matter what happened. Hardening it went in four phases, each one proving the defect on the unmodified code before patching, and ended with a single terminal contract that maps every way a run can end to an honest status and exit code.

Two things about working with AI agents that only show up after you have run them for a while: the cost of what you read at the start of a conversation is paid again on every later turn, and a detailed handoff from another agent is a claim until something the author could not have shaped confirms it.

The free model on my GPU box was unreliable in a way I could not name. Before swapping it, I found the reason by reading its transcripts, then ran a controlled A/B of six scored tests in an isolated copy of the real harness. The candidate won 5 to 2, a longer soak passed 21 of 22, and the swap was one line.

Working with AI assistants for months across several projects, the failure I kept hitting was not bad answers, it was lost context: every conversation restarted from zero or dragged a bloated history. The fix was a method: each conversation does exactly one bounded section, and continuity lives in three small documents, not in the chat.