agent-harness-construction

name agent-harness-construction description Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent's tool set, action space, or observation format. metadata {"origin":"ECC"} Agent Harness Construction Use this skill when you are improving how an agent plans, calls tools, recovers from errors, and converges on completion. Core Model Agent output quality is constrained by: Action space quality Observation quality Recovery quality Context budget quality Action Space Design Use stable, explicit tool names. Keep inputs schema-first and narrow. Return deterministic output shapes. Avoid catch-all tools unless isolation is impossible. Granularity Rules Use micro-tools for high-risk operations (deploy, migration, permissions). Use medium tools for common edit/read/search loops. Use macro-tools only when round-trip overhead is the dominant cost. Observation Design Every tool response should include: status : success|warning|error summary : one-line result next_actions : actionable follow-ups artifacts : file paths / IDs Error Recovery Contract For every error path, include: root cause hint safe retry instruction explicit stop condition Context Budgeting Keep system prompt minimal and invariant. Move large guidance into skills loaded on demand. Prefer references to files over inlining long documents. Compact at phase boundaries, not arbitrary token thresholds. Architecture Pattern Guidance ReAct: best for exploratory tasks with uncertain path. Function-calling: best for structured deterministic flows. Hybrid (recommended): ReAct planning + typed tool execution. Benchmarking Track: completion rate retries per task pass@1 and pass@3 cost per successful task Anti-Patterns Too many tools with overlapping semantics. Opaque tool output with no recovery hints. Error-only output without next steps. Context overloading with irrelevant references.