Tracing AI Inference: A Guide to Debugging Your AI Models and Prompts

AI agents are transforming how we build software, automating complex business processes and creating a new paradigm of "Services-as-Software." But with this power comes a new challenge: opacity. When an AI agent fails, produces a strange result, or runs too slowly, how do you debug it? You're often left staring at a black box, guessing whether the problem was a flawed prompt, a slow external tool, or the model itself hallucinating.

Traditional logging and monitoring tools fall short. They can tell you an error occurred, but they can't show you the why—the intricate dance of prompts, model reasoning, and tool calls that make up an agent's "thought process."

This is where AI observability and tracing come in. By capturing every step of your agentic workflows, tracing turns these black boxes into crystal-clear, debuggable processes. Let's explore how you can use tracing to master the debugging of AI models and prompts.

Why Traditional Observability Isn't Enough for AI

For decades, developers have relied on the three pillars of observability: logs, metrics, and traces. While essential, they were designed for a world of largely deterministic code. AI systems, especially those using Large Language Models (LLMs), are a different beast entirely.

• Lack of Context: A standard log might show the final output of an LLM, but it won't show the exact, fully-rendered prompt that produced it, the model parameters used (like temperature), or the intermediate reasoning steps the model took.
• Complex, Non-Deterministic Paths: An AI agent's execution path isn't fixed. Based on the LLM's output, it might call one tool, then another, or loop back to refine its approach. Visualizing this dynamic flow is nearly impossible with scattered logs.
• Hidden Dependencies: An agent's performance is a function of multiple factors: LLM response time, tool execution speed, and data parsing logic. A single slow step can create a bottleneck, and without a detailed trace, identifying the culprit is pure guesswork.

The Anatomy of an AI Trace

To effectively debug AI, you need a more specialized tool: a trace designed for agentic workflows. An AI trace provides a structured, hierarchical view of a single transaction, from the initial trigger to the final result.

At trace.do, we see every workflow as a collection of spans. A root span represents the entire task, while child spans represent each individual operation within it.

Here’s a simplified example of what a trace for a new user onboarding workflow looks like:

{
  "traceId": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "rootSpanId": "span-001",
  "spans": [
    {
      "id": "span-001",
      "name": "ProcessNewUserOnboarding",
      "durationMs": 2500,
      "attributes": { "user.id": "usr_12345" }
    },
    {
      "id": "span-002",
      "parentId": "span-001",
      "name": "agent.call:VerifyEmail.do",
      "durationMs": 900,
      "attributes": { "result": "verified" }
    },
    {
      "id": "span-003",
      "parentId": "span-001",
      "name": "agent.action:CreateCRMRecord",
      "durationMs": 1400,
      "attributes": { "crm.system": "Salesforce" }
    }
  ]
}

This structure immediately tells a story. The ProcessNewUserOnboarding workflow took 2.5 seconds. Within that, verifying the email took 900ms, and creating the CRM record took 1400ms. This is the foundation of AI observability.

Practical Debugging with Real-time Tracing

Once you have this level of visibility, you can systematically diagnose and solve common AI issues.

1. Debugging Prompts

Problem: Your agent is consistently failing to extract structured data, not following instructions, or giving vague answers.

Solution with Tracing: With a dedicated AI tracing tool like trace.do, you don't just see the final output; you see the exact prompt that was sent to the LLM within a specific span.

• Verify Prompt Integrity: Was a variable interpolated correctly? Did your prompt templating logic accidentally introduce an error? A trace will show you the exact string the LLM received.
• A/B Test Prompts: Run the same workflow with different prompt variations. By comparing the traces, you can see which version leads to faster, more accurate, or more reliable tool usage from the agent. Prompt engineering becomes a data-driven science, not an art.

2. Debugging Model Performance and Cost

Problem: Your workflow is too slow, or your token costs are surprisingly high.

Solution with Tracing: Every span in a trace contains performance metadata.

• Pinpoint Latency: Is the LLM call itself slow, or is the bottleneck an external API call that the agent is waiting on? The durationMs on each span instantly reveals where time is being spent. In our example, the CreateCRMRecord action is the slowest part of the workflow, not an LLM.
• Optimize Cost: A good trace captures token usage (input_tokens, output_tokens) for every LLM call. You can quickly identify which steps are generating excessive tokens and refine the prompts to be more concise, potentially saving significant costs.
• Compare Models: Is GPT-4 Turbo necessary for a simple classification task? By tracing workflows running on different models, you can compare their cost, latency, and output quality to make informed decisions for each specific task.

3. Debugging Agentic Logic

Problem: The agent gets stuck in a loop, fails to use the right tool, or misunderstands the overall user request.

Solution with Tracing: The end-to-end trace visualizes the agent's entire decision-making process.

• Follow the "Thought Process": You can see the sequence of LLM-driven decisions and subsequent tool calls. If the agent fails to call the CreateCRMRecord tool, you can inspect the preceding LLM span to understand why it made that decision. Perhaps its analysis of the user's intent was flawed, pointing to a need for a better prompt.
• Ensure Reliability: For mission-critical "Business-as-Code" workflows, reliability is key. Tracing allows you to see every execution, identify edge cases where the agent's logic fails, and add safeguards to make your automated services more robust.

trace.do: Instant Observability for Your AI Agents

Achieving this level of insight shouldn't require complex, manual instrumentation. The .do platform is built with observability at its core.

trace.do provides instant, real-time tracing for every agentic workflow you run.

• Zero Setup: There's no complex configuration. Simply run your agents on the .do platform, and detailed traces are automatically generated and available in your dashboard.
• Visualize Everything: Go beyond JSON. Visualize the entire workflow, identify bottlenecks at a glance, and dive deep into the attributes of any span to debug issues fast.
• Integrate Seamlessly: Our traces adhere to the open-source OpenTelemetry standard. You can easily export your data to platforms like Datadog, Honeycomb, or New Relic to unify your observability stack.

Stop guessing and start seeing. Turn your AI black boxes into the transparent, reliable, and optimized services you set out to build.