agent-harness-construction

Design agent harnesses for planning, tool-calling, and error recovery.

Updated Mar 31, 2026

Search Tags:#planning #benchmarking #agent #error-recovery #tool-calling #observation

Ready to run

npx skills add https://github.com/GGEdu/claude-god-mode-template --skill agent-harness-construction-ggedu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-harness-construction
Source: https://github.com/GGEdu/claude-god-mode-template/tree/main/skills/agent-harness-construction
Command: npx skills add https://github.com/GGEdu/claude-god-mode-template --skill agent-harness-construction-ggedu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design and optimize how AI agents plan, choose tools, and recover from errors to improve completion rates.

Core Features & Use Cases

Action space design guidelines: use explicit tool names and narrow inputs to ensure deterministic planning.
Observation design: require status, summary, next_actions, artifacts in every tool response to enable reliable orchestration.
Error recovery and context budgeting: define safe retry, minimal prompts, and budgeted context to sustain long-running tasks.
Architecture patterns and benchmarking: guidance for ReAct, function-calling, and hybrid flows; track completion rate and retries.

Quick Start

Initialize a basic agent harness with a minimal set of micro-tools and a simple plan-observe loop.

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 an AI agent harness for reliable tool-calling and planning?▼

Agent harness design uses explicit tool names, narrow inputs, and structured observation schemas with status, summary, next_actions, and artifacts to ensure deterministic planning for reliable orchestration across tool ecosystems.

What is the best way to implement error recovery for AI agents in production pipelines?▼

Error recovery for AI agents requires defining safe retry contracts, minimal prompts, and context budgeting. This sustains long-running tasks within production pipelines through robust error handling.

How does observation schema design affect agent orchestration?▼

Observation schema design requires status, summary, next_actions, and artifacts in every tool response. This structured approach enables reliable orchestration by providing the agent harness deterministic feedback for planning.

How do I benchmark AI agent completion rates and retry behavior?▼

Benchmarking AI agents involves tracking completion rate and retries using built-in hooks. This measures action reliability across architecture patterns like ReAct, function-calling, and hybrid flows.

Can I use context budgeting to sustain long-running agent tasks?▼

Context budgeting sustains long-running agent tasks by defining minimal prompts and budgeted context limits. This prevents context overflow and maintains reliable planning throughout extended production pipelines.

When should I choose ReAct over function-calling architecture patterns for my agent?▼

Choosing between ReAct and function-calling architecture patterns depends on your action space design needs. Both support benchmarking hooks, but ReAct suits reasoning loops while function-calling ensures deterministic tool usage.