agent-harness-construction

Designing an AI agent action space requires stable, explicit tool names and structured inputs and outputs. This approach ensures predictable execution and optimizes overall task completion rates.

AI agent observation formatting should include status, summary, next actions, and artifacts in every tool response. This structured observation design directly supports robust error recovery contracts.

Use the ReAct pattern for exploratory tasks and the hybrid pattern for structured deterministic flows. These architecture patterns guide how the AI agent processes observations and selects actions.

Implement context budgeting by keeping system prompts minimal and moving large guidance into on-demand skills. This strategy optimizes the AI agent context window for better performance.

Common AI agent anti-patterns include unstable tool names, unstructured observation formatting, and missing error recovery contracts. Identifying these pitfalls is crucial for optimizing agent performance.

Benchmark AI agent performance by tracking completion rates, retries, pass rates, and cost per successful task. These metrics provide the necessary data to evaluate action space design and error recovery.