agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting.

16|3|Updated Mar 12, 2026

Search Tags:#design #planning #benchmarking #tooling #agent #error-recovery #observation

Ready to run

npx skills add https://github.com/sehoon787/my-claude --skill agent-harness-construction-sehoon787

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-harness-construction
Source: https://github.com/sehoon787/my-claude/tree/main/skills/ecc/agent-harness-construction
Command: npx skills add https://github.com/sehoon787/my-claude --skill agent-harness-construction-sehoon787

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps engineering teams design and optimize AI agent action spaces, tool definitions, and observation formatting to improve completion rates and reliability.

Core Features & Use Cases

Explicit tooling design for deterministic planning and robust recovery.
Structured observation formats and error-handling strategies to reduce retries.
Use Case: teams building multi-agent orchestration pipelines can apply this skill to standardize tool invocation.

Quick Start

Load the agent-harness-construction skill and begin integrating deterministic planning and tool invocation patterns into your agent workflows.

Frequently Asked Questions about agent-harness-construction

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

FAQPage Schema

How do I optimize AI agent tool use for faster and more reliable results?▼

To optimize AI agent tool use, you must design explicit action spaces, structured tool definitions, and formatted observations. This reduces retries and improves completion rates by enforcing deterministic planning and robust error recovery.

What is the best way to reduce retry rates in multi-agent orchestration pipelines?▼

Reducing retry rates in multi-agent orchestration requires structured observation formats and explicit error-handling strategies. Standardizing tool invocation across the pipeline ensures agents converge on results reliably without excessive failed attempts.

How does observation formatting affect AI agent completion rates?▼

Observation formatting affects completion rates by providing agents with structured, deterministic inputs for planning. Properly formatted observations minimize ambiguity, enabling robust error recovery and reducing the cycles needed to converge on results.

Can I apply ReAct and function-calling patterns to improve agent reliability?▼

Yes, you can apply ReAct, function-calling, and hybrid planning patterns to improve agent reliability. Structured guidance on these patterns standardizes tool invocation and ensures measurable benchmarking across diverse domains.

Do I need measurable benchmarking to ensure multi-agent reliability?▼

Yes, measurable benchmarking is required to ensure multi-agent reliability. Benchmarking validates that deterministic planning patterns and error recovery contracts successfully improve completion rates and tool usage convergence.

Why does my AI agent fail to converge on results in diverse domains?▼

Agents fail to converge when tool definitions and action spaces lack deterministic planning and structured observation formats. Implementing error recovery contracts and explicit tooling design standardizes tool invocation and fixes convergence issues.