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Evaluating an Agent

This guide explains how to evaluate an AI agent using Traces and Chain of Thought, and how these features help you debug, validate, and improve agent performance.

Overview

When an agent generates a response, it goes through multiple internal steps such as reasoning, tool usage, and decision-making.

Kompass provides two powerful capabilities to analyze this:

  • Traces → Step-by-step execution breakdown
  • Chain of Thought → Internal reasoning of the agent

Together, these help you understand how and why an agent produced a response.

Understanding Chain of Thought

Chain of Thought represents the internal reasoning process of the agent while solving a task.

What It Includes

  • Step-by-step reasoning
  • Decision-making logic
  • Tool selection rationale
  • Intermediate thinking steps

Why It Matters

  • Helps you understand why the agent made a decision
  • Improves prompt design
  • Identifies logical errors or hallucinations
  • Increases transparency and trust

Understanding Traces

Traces provide a detailed, step-by-step view of how the agent executed your query.

What Traces Show

  • Input query
  • Execution steps
  • Tool calls (e.g., APIs, MCP tools)
  • Intermediate outputs
  • Final response
  • Execution time

How Traces + Chain of Thought Help in Evaluation

Together, these features give complete visibility into agent behavior:

1. Debugging Issues

  • Trace shows where the issue occurred
  • Chain of Thought shows why it occurred

2. Improving Prompts

  • Identify weak or ambiguous instructions
  • Refine system prompts for better reasoning

3. Validating Tool Usage

  • Ensure correct tools are triggered
  • Detect unnecessary or incorrect API calls

4. Performance Optimization

  • Analyze execution time
  • Reduce unnecessary steps

5. Ensuring Reliability

  • Detect hallucinations
  • Verify factual consistency
  • Improve output quality

Summary

  • Traces = Execution visibility (What happened)
  • Chain of Thought = Reasoning visibility (Why it happened)

Using both together enables: - Faster debugging
- Better prompt engineering
- More reliable agents
- Enterprise-grade observability