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Harness

11 posts on harness.

A status file per bot: how a small office of agents reports in

I run a small office of bots across two runtimes, and I can see every one of them at once: no message bus, no database, one small JSON file each. A dashboard reads that folder every ten seconds and draws the studio as a floor of desks. Here is the whole mechanism, including the freshness rules and the route I had to close on the day I opened it.

Anatomy of an agent harness: hooks, loops, memory, RAG

The model is the one part of an agentic system I did not build and cannot pin down. Everything around it is scaffolding I can test: hooks that block, a loop that verifies, files that remember, a ladder that routes, retrieval that has to stay true. Six components, what each one decides, and the failure that put it there.

LLM model routing: a ladder, a cascade, and a kill switch

LLM model routing is two problems: a ladder that picks the model tier by what a wrong answer costs, and a cascade that answers when a provider dies. Here is how both are built, with the real chain, the real prices, and the paper that explains why the second one will not save you money.

Agentic loops in production: generate, deploy, verify, repeat

Seven weeks after saying loops should run the business, I have two shapes going: one built on a five-minute clock, one that checks in every Monday. The weekly one reads Search Console; here is what the summer taught, including the guardrail that spent two weeks announcing a login prompt as a finished report.

RAG that stays current: knowledge that ships with the feature

Most retrieval systems do not fail at retrieval: they fail because the paragraph they found describes software that no longer exists. Keeping RAG up to date is a scheduling problem before it is an engineering one, so I made it a rule: an admin feature is not done until the migration that ships it has also written the chunks that describe it, and the embed step has run.

Grok Bot, and the question every small business now has to answer

xAI shipped agents that get their own cloud computer and sign into your tools like you do. My feed called it god mode. The interesting part is not the army of bots. It is that renting one and owning one are different bets, and most businesses only need one of them.

Claude Code hooks: the guardrails that never ask the model

A model can be talked out of things: worn down by a long context, swayed by a file it read, or simply wrong at one in the morning. A hook cannot: it is code that runs at a fixed point in the agent loop and returns a decision the model gets no vote on. The hook lifecycle, the exit code that actually blocks, and the two guards I run on every session.

Setting up an AI agent for a realtor who was out of hours

A realtor I work with was never short on leads. She was short on hours. Instead of another dashboard, she got an agent harness: a folder of files on her own Mac that reads everything, drafts carefully, and publishes nothing.

Most of my AI bill was re-reading my own transcript

My usage dashboard blamed subagents and had a fix ready. Twenty minutes of tallying the raw transcripts said the opposite: the ten most expensive sessions of the week spawned no agents at all, and almost every token I paid for was the same conversation being read again.

The SEO skill that has to show its receipts

Most SEO advice written for AI agents is a checklist, which is how you get a confident audit of a site nobody looked at. So the studio built one that cannot make a claim without a fetched artifact behind it, then pointed it at five sites in a day.

The harness reads my texts now

I text myself X posts about agent workflows and never read them twice. So the studio harness got read-only access to the thread, and the pile became three shipped upgrades: a blind duel for risky answers, a knowledge graph under the assistant, and a rulebook half its old weight.

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