What problem does it solve?
Design and optimize how AI agents plan actions, select tools, and format observations to improve plan quality, reliability, and task completion rates.
Core Features & Use Cases
- Architecture pattern guidance: ReAct, function-calling, and hybrid planning to structure exploratory tasks and deterministic tool execution.
- Granularity rules: Use micro-tools for high-risk operations, medium tools for common edit/read tasks, and macro-tools only when round-trip cost dominates.
- Observation design: Enforce consistent response fields such as status, summary, next_actions, and artifacts to enable reliable automation.
- Error recovery contract: Specify root-cause hints, safe retry instructions, and explicit stop conditions for every error path.
- Context budgeting: Keep system prompts minimal and move large guidance into on-demand skills; prefer file references over long inline documents.
- Benchmarking and guardrails: Track completion rate, retries, and pass@1/3; surface anti-patterns to improve reliability and safety.
Quick Start
Describe your target agent task and ask the AI to design an action space, tool usage plan, and observation schema according to the Agent Harness Construction guidelines.
