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agent-harness-construction

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

2|Updated Mar 20, 2026
Search Tags:#planning#architecture#react#tooling#agent#error-recovery#observation-formatting
One-click install
npx skills add https://github.com/multiplex-ai/muggle-ai-teams --skill agent-harness-construction-multiplex-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-harness-construction
Source: https://github.com/multiplex-ai/muggle-ai-teams/tree/main/skills/agent-harness-construction
Command: npx skills add https://github.com/multiplex-ai/muggle-ai-teams --skill agent-harness-construction-multiplex-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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

Core Features & Use Cases

  • Action space design: Define stable, explicit tool names and deterministic input schemas.
  • Observation & recovery: Standardize responses with status, summary, and next actions; provide robust error handling.
  • Architecture patterns: Recommend ReAct, function-calling, or hybrid approaches for structured task execution.

Quick Start

Configure an agent harness for planning and tool usage on a simple task.

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 action space for reliable tool use?▼

AI agent action space design requires defining stable, explicit tool names and deterministic input schemas to ensure reliable tool invocation and improve task completion rates.

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

Formatting agent observations for error recovery involves standardizing responses with explicit status, summary, and next actions to provide robust handling and structured recovery contracts.

How does ReAct compare to function-calling for structured agent planning?▼

ReAct and function-calling are architecture patterns for structured task execution; ReAct interleaves reasoning with actions, while function-calling enforces deterministic input schemas for tool invocation.

Can I control context budgets when building agent workflows for complex decision tasks?▼

Yes, you can control context budgets when building agent workflows by applying structured planning patterns that manage observation formatting and ensure deterministic outputs for complex tasks.

Why does my AI agent fail during tool invocation in complex decision tasks?▼

Agent tool invocation failures often stem from unstable action spaces, lacking deterministic input schemas, or missing explicit recovery contracts for robust error handling.

Do I need explicit recovery contracts for agent error handling?▼

Explicit recovery contracts are needed for agent error handling to standardize observation responses, define next actions, and ensure robust completion rates in complex decision workflows.

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