harness-design

A reliable agent harness defines the tool space, observation format, and evaluation signals before writing prompts. You structure it across five layers—action space, tool boundaries, observation, evaluation, and iteration—to enforce strict input/output contracts and enable repeatable improvements.

LLM agent misbehavior often stems from unclear tool boundaries and observation scope. By applying strict per-action logging, error handling, and clear input/output contracts, you can diagnose exactly where the agent loop fails and guide iterative improvements.

The best way to structure evaluation signals is to define them as a dedicated harness layer alongside action space and observation format. This ensures you capture specific success metrics for each layer, enabling repeatable diagnostics and feedback mechanisms during iteration.

To debug a failing LLM agent harness, outline its five layers—action space, tool boundaries, observation, evaluation, and iteration—and document the inputs, outputs, and success metrics for each. This pinpoints contract violations and guides targeted fixes.

Yes, strict input/output contracts are essential for production-grade agent loops. They ensure reliable action execution, enable accurate per-action logging, and provide the diagnostics needed to maintain stability across tool space and data flow.

A five-layer harness design may be unnecessary for simple, single-tool tasks with minimal data flow. It is specifically built for designing new harnesses, debugging complex agents, and hardening production-grade loops involving multiple tools and feedback mechanisms.