5 posts tagged with "Agent Harness"

Every AI platform says it is model-agnostic. Almost none of them are. Not because the teams are lying — because agnosticism isn't a property you declare in a README. It's a seam in the code, and a seam that nothing routinely crosses is a seam that has already rusted shut. You just don't find out until the day you try to move it.

Everyone who designs an agent loop spends their time on two steps. They tune the generate step, because that's where the intelligence appears to live. They harden the verify step, because that's where the discipline lives. Almost nobody budgets serious design effort for the third step, which is the one that actually determines whether the loop is trustworthy: the stop.

Here is the uncomfortable mechanical fact underneath every loop you have ever built. A turn that produces text with no tool call is the only place a run can stop. That's it. That's the entire exit condition — the absence of a function call. And that single signal conflates three completely different situations, which is why agents routinely "finish" tasks they never did.

This post assumes you already have a loop — generate, verify, correct — and that it has burned you at least once. We're going one level down, into the exit.

Ask an engineer how to stop an agent that won't stop, and you'll get the same answer every time: add a limit. It's the right instinct and the wrong plan. There is no single limit that catches every shape of runaway, because runaways don't have one shape — and each shape slips past a different guard for a different structural reason.

A skill is a self-contained Markdown directive with a little YAML frontmatter on top, sitting in its own directory. That description makes it sound like documentation, which is exactly the trap. The interesting part is not what a skill contains. It's when the agent pays for it.

Here is the sentence we keep in our own skill registry, and it is the whole design: a skill is a directive the agent reads on demand via a read_skill tool, rather than something injected into every prompt — so the agent pays the token cost only when the skill is actually relevant to the turn.

The most expensive misconception in applied AI is that performance lives in the model. Pick the best model, the thinking goes, and the rest is glue. So teams spend their attention on which model to call and treat everything around it — the tools, the loop, the context they assemble, the way they check the output — as plumbing.