How to Analyze AI Agent Traces Like a Detective

The Final Output is Just the Tip of the Iceberg

When an AI agent fails, the natural instinct is to look at the final, incorrect output and try to figure out what went wrong. This is like a detective arriving at a crime scene and only looking at the victim, ignoring all the surrounding evidence.

To truly understand and fix agent failures, you need to become a detective and investigate the trace. A trace is the complete, step-by-step log of everything the agent did during an interaction:

• Every internal thought or reasoning step.
• Every tool it decided to call.
• The exact parameters it used for each tool call.
• The raw output it received from each tool.
• Every decision it made based on new information.

Analyzing these traces is the single most powerful debugging technique for AI agents.

A 6-Step Framework for Trace Analysis

Here's a systematic approach to dissecting an agent trace to find the root cause of any failure.

Step 1: Understand the Goal

First, clearly define what a successful outcome would have looked like. What was the user's intent? What was the agent supposed to do according to its system prompt?

Step 2: Follow the Trajectory

Start from the beginning of the trace and walk through each step of the agent's reasoning process. Don't make assumptions. Read the agent's internal monologue. Does its chain of thought make logical sense?

Step 3: Identify Key Decision Points

Pinpoint the exact moments where the agent made a choice. This could be deciding which tool to use, what parameters to pass, or how to interpret a tool's response. Was the choice it made the optimal one?

Step 4: Scrutinize Tool Calls

This is often where things go wrong. For every tool call, ask:

• Was this the right tool for this specific sub-task?
• Were the parameters passed to the tool correct and well-formed?
• Was the tool's output what you expected? Did the agent handle an error or unexpected output gracefully?

Step 5: Check for Compliance and Constraint Violations

At each step, cross-reference the agent's action with its system prompt. Did it violate any of its core instructions? For example, if it's not supposed to give financial advice, did it call a stock price API?

Step 6: Pinpoint the Root Cause

By following these steps, you can move beyond simply identifying the failure and pinpoint its origin. Was the root cause:

• A Reasoning Error? The agent's logic was flawed.
• A Tool Use Error? The agent used the wrong tool or used it incorrectly.
• A Prompt Issue? The system prompt was ambiguous or incomplete.
• A Model Limitation? The underlying LLM simply wasn't capable of the required reasoning.

A Practical Example

Imagine a customer support agent that gives a user the wrong refund amount. The trace might reveal:

• Agent correctly understands the user's request for a refund.
• Agent correctly calls the getOrderDetails tool with the right order ID.
• The tool returns the correct order data, including price: 99.99 and discount: 10.00.
• The agent's reasoning step says: "The refund amount is the price. I will refund $99.99."
• Root Cause: A reasoning error. The agent failed to account for the discount.

Now you know exactly what to fix. You don't need to debug the tool or the data. You need to improve the agent's reasoning, likely by updating the system prompt to explicitly mention how to handle discounts.

Without trace analysis, you're just debugging in the dark.

To streamline your trace analysis process, Noveum.ai's Debugging and Tracing solution provides hierarchical trace visualization and automated root cause analysis.

Have you ever analyzed an agent trace to find a surprising root cause? Share your story!