What problem does it solve? AI agents often fail because of poorly designed action spaces, opaque tool outputs, and missing error recovery paths. This Skill provides a structured framework for designing agent harnesses so agents plan, call tools, recover from errors, and converge on task completion more reliably. ## Core Features & Use Cases - Action Space Design: Rules for stable tool naming, schema-first narrow inputs, deterministic output shapes, and granularity guidance (micro, medium, and 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: When to use ReAct, function-calling, or hybrid patterns, and which metrics to track (completion rate, retries, pass@1/pass@3, cost per task). - Use Case: You are building a coding agent whose tool calls frequently fail silently. Apply this Skill to redesign tool outputs with structured status and next_actions fields, then measure completion rate improvements. ## Quick Start Use the agent-harness-construction skill to review my agent's tool definitions and improve its error recovery and observation formatting.