agent-harness-construction

Design agent action spaces, tool definitions, and observation formats.

1|Updated Mar 10, 2026

Search Tags:#agent #tool design #error recovery #harness #observation #action space

Ready to run

npx skills add https://github.com/aleonsa/claude-config --skill agent-harness-construction

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-harness-construction
Source: https://github.com/aleonsa/claude-config/tree/main/claude/skills/agent-harness-construction
Command: npx skills add https://github.com/aleonsa/claude-config --skill agent-harness-construction

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the core challenges in AI agent development by improving how agents interact with tools and process information, leading to higher success rates.

Core Features & Use Cases

Action Space Design: Ensures stable, explicit tool names and schema-first inputs for predictable agent behavior.
Observation Formatting: Standardizes tool responses with status, summary, next_actions, and artifacts for better error handling and debugging.
Error Recovery: Defines clear contracts for handling errors, providing hints, retry instructions, and stop conditions.
Context Budgeting: Optimizes prompt and reference management to stay within token limits efficiently.
Use Case: When an agent is failing to complete tasks due to ambiguous tool outputs or poor error handling, this skill can be invoked to refine the agent's interaction patterns.

Quick Start

Use the agent-harness-construction skill to refine the action space and observation format for the current agent.

Frequently Asked Questions about agent-harness-construction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design AI agent action spaces to improve task completion rates?▼

AI agent action spaces improve task completion rates by using stable tool names and schema-first inputs to ensure predictable behavior. Standardizing observation formatting and error recovery contracts further increases success rates.

Why does my AI agent fail when handling ambiguous tool outputs and poor errors?▼

Agents fail on ambiguous tool outputs because they lack standardized observation formatting and clear error recovery contracts. Defining contracts with status, summary, next_actions, and retry hints resolves these interaction failures and improves debugging.

What is the best way to format AI agent observations for error recovery?▼

The best way to format agent observations is standardizing tool responses with status, summary, next_actions, and artifacts fields. This structured formatting provides clear hints, retry instructions, and stop conditions for robust error recovery.

How do I manage context budgeting for ReAct and Function-calling agent patterns?▼

Context budgeting for ReAct and Function-calling patterns is managed by optimizing prompt and reference handling to stay within token limits. This architecture pattern ensures efficient context distribution alongside well-defined action spaces and deterministic tool outputs.

Does standardizing tool definitions work with existing agent architecture patterns?▼

Standardizing tool definitions works with agent architecture patterns by recommending ReAct and Function-calling implementations. Schema-first inputs and deterministic outputs integrate with these patterns to provide stable tool names for predictable agent behavior.

When do I need to define error recovery contracts for AI agent tools?▼

Error recovery contracts for AI agent tools are needed when agents struggle with ambiguous outputs or poor error handling. Defining clear contracts with hints, retry instructions, and stop conditions ensures stable tool usage and higher task completion rates.