Harness Optimization
An LLM agent’s behavior is not determined when its model is selected. What enters context, how tools are provided, when outputs are verified, and how the agent recovers from failure all matter. Even with the same model, the surrounding execution system changes which tasks it can solve, its cost, and its failure modes. This book calls that execution system, comprising prompts, memory, control flow, validators, and recovery logic, an executable harness.
Can the harness itself be improved from execution records and evaluations without changing the model? This book treats edits to prompts, workflows, memory, and runtime code as a single optimization loop. It generates candidate updates from failures, identifies real improvements under limited budgets and noisy evaluations, and carries only verified changes forward to the next execution. The goal is not for the agent merely to change. The goal is to make that change a reproducible, reliable improvement.