Visualizing AI: How to Monitor and Debug Agentic Workflows

The rise of AI and agentic workflows is transforming how applications are built. These intelligent systems, capable of executing complex, multi-step tasks, can interact with various APIs, databases, and large language models (LLMs) to achieve a single goal. But with this power comes a new level of complexity. When an agentic workflow slows down or fails, how do you find the root cause? It's like trying to find a single faulty wire in a city-sized circuit board.

This is where the principles of tracing and observability become not just helpful, but essential. You need to Understand Every Action your system takes. Without a clear view into the operational flow, you're debugging in the dark.

The Challenge of the "Black Box" Workflow

Agentic workflows are often seen as "black boxes." A request goes in, and after a series of invisible steps, an answer comes out. The challenges in monitoring these systems include:

• Distributed Complexity: A single task might involve calls to an authentication service, a vector database, an external API, and one or more LLMs. A problem in any one of these can impact the entire workflow.
• Performance Bottlenecks: Is the workflow slow because of a complex database query, a slow external API, or the LLM's response time? Without detailed timing, it’s all guesswork.
• Error Pinpointing: When an error occurs, was it a data validation issue, a failed API call, or an unexpected response from a service? Locating the exact point of failure is critical for a fast resolution.

To solve this, you need to shed light on every step of the journey. You need to visualize your workflow.

Deconstructing the Workflow with Tracing

A trace is a complete, end-to-end record of a single request or operation as it moves through your system. It's composed of "spans," where each span represents a specific unit of work—like an API call or a database query.

By examining a trace, you can see the entire lifecycle of a request, including the relationships between services and the duration of each step.

Consider this example trace from trace.do:

{
  "traceId": "a1b2c3d4e5f67890",
  "traceName": "/api/user/profile",
  "startTime": "2023-10-27T10:00:00.000Z",
  "endTime": "2023-10-27T10:00:00.150Z",
  "durationMs": 150,
  "spans": [
    {
      "spanId": "span-001",
      "parentSpanId": null,
      "name": "HTTP GET /api/user/profile",
      "service": "api-gateway",
      "durationMs": 150,
      "status": "OK"
    },
    {
      "spanId": "span-002",
      "parentSpanId": "span-001",
      "name": "auth-service.verifyToken",
      "service": "auth-service",
      "durationMs": 25,
      "status": "OK"
    },
    {
      "spanId": "span-003",
      "parentSpanId": "span-001",
      "name": "db.query:SELECT * FROM users",
      "service": "user-service",
      "durationMs": 110,
      "status": "OK"
    }
  ]
}

Even in this simple JSON format, we can immediately understand the story of this request:

1. The entire operation took 150ms.
2. It started at the api-gateway.
3. The first child operation was a token verification in auth-service, which took 25ms.
4. The second child operation was a database query in user-service, which took 110ms.

Instantly, we can see that the database query is the most time-consuming part of this workflow. This is the power of tracing: it turns guesswork into data-driven insight.

trace.do: Effortless Tracing for AI and Business Workflows

trace.do is a powerful agentic workflow tool designed to provide deep insights into your application's performance. It delivers comprehensive tracing and observability specifically tailored for monitoring modern AI and business workflows, allowing you to debug, monitor, and optimize your systems with ease.

With trace.do, you can effortlessly trace requests and operations across your distributed systems. This complete visibility allows you to pinpoint bottlenecks and resolve issues faster than ever before.

How Does It Work?

Getting started with trace.do is designed to be seamless.

• Seamless Integration: Using the .do SDK, you can automatically or manually instrument your code to create and propagate traces with minimal configuration.
• Open Standards Compatibility: Built to be compatible with open standards, trace.do works with OpenTelemetry. This means you can easily ingest data from your existing instrumented services, consolidating all your observability data into one unified platform.
• Detailed Information: A trace gives you more than just timing. It provides a detailed view of a request's lifecycle, including metadata, logs, and critical relationships between services, allowing you to pinpoint the exact source of delays or errors.

Don't Debug in the Dark

As AI and agentic systems become more integrated into our digital infrastructure, observability is no longer a luxury—it's a core requirement for building robust, reliable, and performant applications. Tools that provide clear, actionable insights are essential for any team working on the cutting edge.

By visualizing your workflows, you can move from reactive debugging to proactive optimization, ensuring your AI systems perform exactly as intended.

Ready to gain complete visibility into your AI and business workflows? Visit trace.do to learn how to understand every action.