agent-harness-construction

Name: agent-harness-construction
Availability: InStock
Author: samymity

Restructure tool action spaces with schema-first inputs and structured responses.

1|Updated Mar 3, 2026

Search Tags:#ai agents #error recovery #function calling #tool definitions #context budgeting #reliability engineering #observation schema

Ready to run

npx skills add https://github.com/samymity/bridge-ventures-backend --skill agent-harness-construction-samymity

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams improve an AI agent’s ability to complete tasks reliably by making its action space, tool outputs, and error recovery behavior explicit and consistent.

Core Features & Use Cases

Action space design: Define stable tool names and narrow, schema-first inputs with deterministic output shapes to reduce ambiguity.
Observation and recovery contracts: Require each tool response to include status, summary, next_actions, and artifacts, plus explicit error recovery instructions and stop conditions.
Context budgeting and architecture guidance: Keep the system prompt minimal, move large guidance into on-demand skills, and select ReAct vs function-calling vs a hybrid approach based on task structure.

Quick Start

Use the agent-harness-construction skill to redesign your agent’s tool list, observation schema, and error recovery contract for a higher completion rate on repeated task runs.

Frequently Asked Questions about agent-harness-construction

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

FAQPage Schema

How do I improve AI agent reliability during error-prone task execution?▼

Improve AI agent reliability by restructuring tool action spaces and enforcing consistent observation and recovery contracts. This requires explicit tool naming, schema-first narrow inputs, deterministic output shapes, and structured tool responses with status, summary, next_actions, and artifacts.

What is the best way to design tool observations for function calling agents?▼

Design tool observations for function calling agents by enforcing a structured response contract. Each tool response must include status, summary, next_actions, and artifacts, alongside explicit error recovery instructions and safe retry plus stop conditions to reduce ambiguity.

How do I manage context budgeting for LLM agents with complex planning workflows?▼

Manage context budgeting for LLM agents by keeping the system prompt minimal and moving large guidance into on-demand skills. Select ReAct, function-calling, or a hybrid approach based on task structure to optimize planning and execution loops.

Why does my AI agent fail to recover from tool calling errors in deployment operations?▼

AI agents fail to recover from tool calling errors when lacking explicit recovery contracts. Define safe retry behaviors, explicit stop conditions, and deterministic output shapes to ensure consistent error handling across deployment, migration, and permissions operations.

When should I use ReAct versus function-calling for my AI agent architecture?▼

Choose ReAct versus function-calling based on task structure complexity. Use this approach to redesign your agent's tool list and observation schema, selecting a hybrid architecture when balancing planning, execution, and edit, read, or search loops.

Does agent-harness-construction work for migration and permissions operations?▼

Agent-harness-construction applies to tool-using LLM agents in error-prone workflows across deployment, migration, and permissions operations. It redesigns tool action spaces and observation contracts to achieve a higher completion rate on repeated task runs.