What problem does it solve? AI agents often fail tasks because of poorly designed tool interfaces, opaque tool outputs, and missing error recovery guidance. This Skill provides a structured methodology for designing action spaces, observation formats, and recovery contracts so agents complete tasks more reliably. ## Core Features & Use Cases - Action Space Design: Rules for stable tool naming, schema-first narrow inputs, deterministic outputs, and granularity guidance (micro, medium, macro tools). - Observation & Recovery Contracts: Standard response fields (status, summary, next_actions, artifacts) plus error contracts with root cause hints, safe retries, and stop conditions. - Architecture & Benchmarking Guidance: Recommendations on ReAct vs function-calling vs hybrid patterns, context budgeting, and metrics like completion rate, pass@1, and cost per successful task. - Use Case: When building a coding agent whose tool calls frequently fail, apply this Skill to redesign tool schemas and add structured error outputs so the agent can self-correct instead of stalling. ## Quick Start Use the agent-harness-construction skill to review my agent's tool definitions and improve its error recovery and observation formatting.