What problem does it solve?
Poorly designed AI agent harnesses lead to low task completion rates, high retry counts, and inefficient token usage due to unstructured action spaces, uninformative tool observations, and missing error recovery logic.
Core Features & Use Cases
- Action Space Design: Guidelines for stable tool naming, schema-first narrow inputs, deterministic output shapes, and granularity rules for micro/medium/macro tools based on operation risk.
- Observation Standardization: Mandatory tool response fields (status, summary, next_actions, artifacts) to ensure consistent, actionable agent feedback.
- Error Recovery Contracts: Defined structure for error paths including root cause hints, safe retry instructions, and explicit stop conditions to reduce failed task runs.
- Context Budgeting: Rules to minimize system prompt size, load guidance on demand, and compact context at phase boundaries to optimize token usage.
- Use Case: A development team building a coding agent can use this skill to define granular tools for code edits and deployments, standardize tool response formats, and implement error recovery for failed build steps to improve overall task success rates.
Quick Start
Use the agent-harness-construction skill to redesign your AI agent's tool definitions and observation formatting to improve task completion rates.
