agent-harness-construction

The agent-harness-construction skill focuses on designing and optimizing the frameworks through which AI agents operate, particularly in terms of action spaces, tool interactions, and observation handling. By improving these core components, it addresses common challenges such as inconsistent agent behavior, inefficient task completion, and poor error recovery. This skill provides structured guidance for ensuring that AI agents can execute tasks with higher reliability and precision, while maintaining clear communication and feedback loops during operations.

Key features of this skill include carefully structured action spaces, clearly defined observation formats, and robust error recovery protocols. It emphasizes the use of explicit, stable tool names, schema-first inputs, deterministic outputs, and appropriate tool granularity based on task risk. Observations are designed to include concise status indicators, actionable next steps, and artifact references. The skill also provides guidelines for context budgeting, recommending minimal system prompts, on-demand skill loading, and references to external files instead of inlining large content. Additionally, it outlines architectural patterns, such as ReAct for exploratory tasks, function-calling for deterministic flows, and hybrid approaches combining planning with typed tool execution, along with benchmarking metrics to track completion rates, retries, and task efficiency.

This skill is particularly valuable for AI developers, ML engineers, and teams building complex agent-driven systems. It supports use cases where agents need to navigate multi-step processes, interact with various tools, handle errors gracefully, and optimize completion outcomes. It is suited for environments where reliability, task efficiency, and structured agent behavior are critical, such as automated deployments, document processing, knowledge management, and software orchestration tasks.