LLM

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.

Every mode of my study assistant returned a server error before the model was ever called. The context packet's fixed part had grown past the safety limit on its own, and one unbounded field guaranteed it would keep growing. The fix went into the shared layer three different callers depend on, with caps chosen by measurement and a degrade path before any error.

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.

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.