What is Agent Optimization?
Closing the loop — using what observability and evaluation tell you to actually make the agent better, then proving it with the next eval.
Agent optimization is the practice of improving an AI agent in production using the evidence that observability and evaluation produce — then re-evaluating to confirm the change worked. It is the third stage of the loop: observe what the agent did, evaluate whether it was good, and optimize what wasn't. Unlike prompt or model optimization, which tune a component in isolation at dev time, agent optimization improves the whole agent against real production failures, continuously.
Agent optimization is closing the loop
Observability shows you what an agent did. Evaluation tells you whether it was good. Optimization is what you do with that — the step that turns measurement into a better agent.
The loop runs: trace real production sessions (observe), score them against what they should have done (evaluate), change the prompt, tool, retrieval or model to fix what scored badly (optimize), and re-evaluate against the now-larger dataset to confirm the fix stuck without breaking anything else. Because the dataset grows from real failures, every pass makes the agent measurably better at the job it actually faces — not the job you imagined at launch.
Agent optimization vs prompt and model optimization
They are related but operate at different layers. Prompt optimization, fine-tuning and LLM optimization tune a component — a prompt, a model — usually in isolation, at development time, against a fixed benchmark. They are techniques.
Agent optimization improves the whole agent — its prompts, tools, retrieval, memory and routing together — against real production evidence, and verifies the result with evals on live behaviour. You can optimize a prompt to perfection and still have an agent that calls the wrong tool or loses the thread. So prompt and model optimization sit *underneath* agent optimization: they are some of the levers you pull, but the agent is the thing you are actually trying to make reliable.
The levers — and where each one fits
When an eval surfaces a failure, you have a menu of changes, from cheapest to most expensive:
Prompt optimization — rewrite or systematically tune the instructions; fastest, reversible. Human-in-the-loop — route low-confidence or high-risk steps to a person, and feed their corrections back as training signal. Retrieval and tool changes — fix what the agent can see and do, which is often the real cause of a bad answer. Fine-tuning — train the model on your corrected cases when prompting has hit its ceiling; powerful but slow and harder to roll back.
The discipline is to let the eval point at the cheapest lever that fixes the failure, change one thing, and re-evaluate — rather than fine-tuning when a prompt fix would do.
Eval-driven development: the operating rhythm
Teams that improve agents reliably converge on the same rhythm: write the eval for a behaviour or a known failure before the fix, and ship the change only when it passes the suite without breaking the rest. It is test-driven development applied to non-deterministic agents.
This is what makes optimization safe at speed. Every change is scored against the same cases as the last change, so 'did the agent get better?' becomes a trend you can plot rather than an argument in chat — and the loop, not any single tweak, is what compounds into a reliable agent.
See how Prefactor closes the loop — evaluate, improve, re-evaluate
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