What problem does it solve?
Poorly designed AI agent harnesses—with ambiguous tool definitions, uninformative output formatting, and weak error handling—lead to low task completion rates, high retry counts, and wasted compute resources. This Skill eliminates those bottlenecks by providing proven, actionable patterns for building robust, efficient agent systems.
Core Features & Use Cases
- Action Space Design: Creates stable, granular tool schemas scaled to operation risk (micro-tools for high-risk actions like deployments, medium tools for common edit/read loops, macro-tools for low-overhead round trips) to reduce agent ambiguity and improve execution accuracy.
- Observation & Recovery Standards: Establishes consistent tool output formats with status, summary, next actions, and artifacts, plus explicit error recovery contracts with root cause hints, safe retry instructions, and clear stop conditions.
- Context & Architecture Optimization: Provides guidance for minimizing system prompt bloat, loading large guidance on demand, selecting between ReAct, function-calling, and hybrid agent architectures, and tracking key performance metrics like completion rate and cost per successful task.
- Use Case: A team building a code review agent can use this Skill to define granular lint/edit/test tools, format review outputs with actionable fix suggestions, and implement retry logic for flaky test runs to reduce failed review cycles by 40% or more.
Quick Start
Use the agent-harness-construction skill to redesign your AI agent's tool definitions, observation formatting, and error recovery flows to increase first-pass task completion rates and reduce unnecessary retries.
