I took on this project not knowing much about AI orchestration or harness engineering. A few months in, they are the two things I think about most. This is what I learned, written for whoever is about to fall down the same rabbit hole.
The project is an autonomous agent at Eko that resolves real support work against a live financial system, end to end, with no human clicking go. I had assumed the hard part would be the model, the prompting, the cleverness. It is not. The model is the easy part. Everything around it is the job, and that everything has a name: the harness.
What harness engineering actually is
A harness is all the scaffolding that turns a smart but unreliable model into a system you can trust to run on its own. It is the orchestration loop that decides what happens and in what order. It is the retries and the recovery when a step fails. It is the deterministic code that holds the authority so the model never has to. It is the evaluation that tells you whether a change made things better or worse. None of that is model work. All of it is what separates a demo from something that runs in production against real money.
Before this I had helped build an agent on a lexical architecture with a knowledge base, keyword matching to route requests and retrieval to fetch context. Rebuilding it into what we run now is where most of my learning happened, so let me go through the parts that changed how I think.
