Why Tracing Is the New Foundation for Building Reliable AI Agents

You launch your agent, and yesterday it worked perfectly. Today it crashes.

You pull up the logs and scan the codebase. Maybe it’s hallucination. Maybe the context window overflowed. But the real problem is this: you can’t tell.

That’s because you’re still debugging agents like traditional software — and that mindset is already obsolete.

In order to understand, debug, and iterate on your agents, you need something new. You need tracing.

Traditional Software vs. AI Agents

With traditional software, behavior is predictable. If we process a refund, we know the sequence:

1. Card refunded
2. Ledger updated
3. User notified

Every step is defined in code. If something breaks, we inspect logs and trace the issue back to a specific line.

Agents don’t work this way.

Why Agents Demand Tracing

With AI agents:

• The same input can lead to different reasoning paths
• Agents may call different tools
• Outcomes can vary from run to run

In this world, our code becomes scaffolding. We define:

• The model
• The tools
• The prompt

…but we no longer decide the specific path or decisions the agent takes. That’s driven by the model.

So when things go wrong, where should we look? We need insight into how our agent makes decisions — and that’s where tracing comes in.

What Is Tracing?

We can’t see how a model reasons internally — but we can observe what it does. Every prompt, every step, every tool call, every message leaves measurable signals.

Combined, these signals reconstruct the sequence of actions an agent takes — this is called a trace.

A trace includes:

• Model reasoning at each step
• Tools called and their parameters
• Outputs generated
• Timing information
• Cost impacts

Conversations and Threads

Agents often interact with users in a conversation. Each message in the conversation creates a new trace.

These traces are grouped into a thread — the full history of the conversation. Threads let you see how agent behavior evolves across multiple turns.

So when something goes wrong with your agent, the answer isn’t in the code — it’s in the trace or the thread.

How Tracing Changes Engineering

Tracing should transform how you build AI agents.

1. Debugging Has Become Trace Analysis

When an agent behaves unexpectedly, the logic you’re looking for isn’t in the code — it’s in the trace.

Trace analysis becomes the new debugger.

2. Evals Replace Unit Tests

Because agent logic lives in traces, you test those traces — not individual functions.

You run:

• Evals on past traces (to verify expected behavior)
• Evals on live traces (to monitor quality over time)

This is how we measure agent performance in production.

3. Product Analytics Has Become Trace Analytics

The same traces developers use to debug also reveal:

• How users interact with your agent
• Where users get stuck
• What patterns emerge in real usage

Trace analytics uncovers usage patterns, friction points, and failure modes — all based on actual behaviors, not hypothetical tests.

Observability: From Exhaust to Fuel

In traditional software, observability is the “exhaust” you monitor.

With agents, observability becomes fuel — the data that powers every improvement workflow.

This means your observability platform — your tracing system — should become the center of collaboration across teams.

Conclusion

The next time your agent behaves unexpectedly, don’t ask to see the logs.

Ask to see the trace.

Because with agentic AI, traces are the only reliable way to understand, debug, optimize, and evolve your systems.

Tracing isn’t just a helpful tool — it’s the foundation of agent engineering in the AI era.