agent-harness-construction

Optimize AI agent action spaces by using stable, explicit tool names, schema-first inputs, and deterministic output shapes. Avoid catch-all tools unless isolation is impossible, ensuring structured observations for higher task completion rates.

AI agent tool observation formatting should include status, summary, next_actions, and artifacts. Every tool response must explicitly provide these structured elements to guide subsequent agent steps and prevent unhandled execution errors.

Design error recovery contracts by including a root cause hint, safe retry instruction, and explicit stop condition for every error path. This prevents opaque outputs and ensures agents converge on completion instead of looping.

The recommended architecture pattern is a Hybrid approach combining planning with typed tool execution. This guides how agents plan, call tools, and recover from errors more reliably than standard ReAct or Function-calling alone.

Agents fail to converge due to anti-patterns like too many tools with overlapping semantics or context overload with irrelevant references. Consolidate tool semantics and keep system prompts minimal to improve completion rates.

Benchmark AI agent reliability by tracking completion rate, retries per task, pass@1, pass@3, and cost per successful task. These metrics quantify whether your action space design and observation formatting improvements are effective.