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agent-harness-construction

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

3|Updated Apr 18, 2026
Search Tags:#error-recovery#function-calling#agent-design#tool-definition#observation-format#react-planning
One-click install
npx skills add https://github.com/nassimbf/ftitos-claude-code --skill agent-harness-construction-nassimbf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-harness-construction
Source: https://github.com/nassimbf/ftitos-claude-code/tree/main/skills/agent-harness-construction
Command: npx skills add https://github.com/nassimbf/ftitos-claude-code --skill agent-harness-construction-nassimbf

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design and optimize AI agent action spaces, tool definitions, and observation formatting to improve completion rates.

Core Features & Use Cases

  • Action-space design: establish stable, explicit tool names and narrow inputs to minimize ambiguity.
  • Observation & recovery: define consistent response schemas and robust error handling for recovery.
  • Architecture guidance: balance planning methods (ReAct, function-calling, hybrid) with guardrails and benchmarking.
  • Use Cases: accelerate development of autonomous agents that plan, execute, and recover autonomously.

Quick Start

Define an action-space design for a new agent workflow and outline tool interfaces, observation fields, and recovery steps.

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 agent action space to improve task completion rates?▼

Designing an agent action space requires establishing stable, explicit tool names and narrow inputs to minimize ambiguity, ensuring deterministic tool calls and structured input for better completion rates.

What is the best way to define observation formats for ReAct planning?▼

Observation formats for ReAct planning are defined by establishing consistent response schemas and robust error handling mechanisms, which enable autonomous agents to recover reliably from execution failures.

How do I implement error recovery in function-calling workflows?▼

Error recovery in function-calling workflows is implemented by defining robust error handling strategies and consistent observation fields, allowing the agent to plan, execute, and recover autonomously during tool use.

Does this approach support balancing ReAct planning with function-calling guardrails?▼

Yes, this approach provides architecture guidance to balance planning methods like ReAct and function-calling with guardrails, applying structured input and measurable performance benchmarks to optimize workflows.

Why does my AI agent fail to recover after a tool call returns an error?▼

Agents fail to recover when observation formats and error handling schemas are inconsistent, making it necessary to define explicit tool interfaces and robust recovery steps to handle execution failures.

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