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observability-decision-tracing

Trace AI agent decision-making processes, capturing options, choices, context, and outcomes.

Updated Jun 21, 2026
Search Tags:#tool selection#AI debugging#agent reasoning#decision tracing#decision quality
One-click install
npx skills add https://github.com/infantesromeroadrian/arca-agent --skill observability-decision-tracing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: observability-decision-tracing
Source: https://github.com/infantesromeroadrian/arca-agent/tree/main/template/skills/observability-decision-tracing
Command: npx skills add https://github.com/infantesromeroadrian/arca-agent --skill observability-decision-tracing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps in understanding the decision-making process of agents, enabling debugging of failures and optimizing decision quality.

Core Features & Use Cases

  • Decision Tracing: Capture what options were available, what was chosen, the context, and whether it was correct.
  • Tool Selection Tracing: Track which tools were selected and why, including parallel tool calls and reasoning.
  • Routing Decision Tracing: Analyze agent/model routing decisions, including available agents and scores.
  • Chain of Thought Tracing: Extract and trace reasoning from agent responses, including structured output.
  • Multi-Step Decision Tracing: Log full decision chains and evaluate decision quality after outcomes.
  • Use Case: Use this Skill to debug an agent's decision-making process when it fails to complete a task as expected.

Quick Start

Use the observability-decision-tracing skill to trace the decision-making process of an agent during task execution.

Frequently Asked Questions about observability-decision-tracing

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

FAQPage Schema
How do I trace AI agent decisions and reasoning for debugging?▼

You can trace AI agent decisions by capturing available options, choices, context, and outcomes. Decision tracing logs the full decision chain to help debug failures and optimize agent performance in complex scenarios.

What is the best way to debug why an AI agent selected a specific tool?▼

Tool selection tracing captures which tools an agent selected and why, including parallel tool calls and reasoning. This allows you to analyze tool selection logic and identify why incorrect tools were chosen during task execution.

How does chain of thought tracing work for analyzing agent responses?▼

Chain of thought tracing extracts and traces reasoning directly from agent responses, including structured output. It captures the step-by-step logic an agent uses, enabling you to evaluate decision quality after outcomes occur.

Can I analyze agent routing decisions and scores during multi-step execution?▼

Yes, routing decision tracing analyzes agent and model routing decisions, including available agents and scores. Multi-step decision tracing logs full decision chains across execution, allowing you to evaluate routing quality after task completion.

When do I need decision tracing for my AI agent optimization?▼

You need decision tracing when an agent fails to complete a task as expected and you must understand its decision-making process. It applies to debugging failures and optimizing decision quality in complex, multi-step decision-making scenarios.

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