agent-harness-construction

Optimize AI agent action spaces by designing stable tool names, schema-first inputs, and deterministic output shapes. Structured action space design ensures explicit tool definitions and reduces error paths, directly improving task completion rates.

AI agent observation design must include status, summary, next_actions, and artifacts in every tool response. Structured observation formatting prevents context overloading and ensures the agent receives clear, actionable feedback without opaque outputs.

An error recovery contract provides root cause hints, safe retry instructions, and explicit stop conditions for AI agents. This structured recovery mechanism guides the agent away from failure loops and back to successful task completion.

Manage context budgeting by using minimal system prompts, storing large guidance in skills, and compacting context at phase boundaries. This strategy works across ReAct, Function-calling, or Hybrid architecture patterns to maintain agent efficiency.

Benchmark AI agent performance by tracking completion rate, retries, pass@1, pass@3, and cost per successful task. These metrics reveal how well your action space design and observation formatting reduce errors and improve efficiency.

Common AI agent anti-patterns include exposing too many tools, generating opaque outputs, and context overloading. These pitfalls degrade agent performance by increasing error paths and reducing overall task completion rates.