From the course: Creating Agents with Python and the AI Toolkit for Visual Studio Code
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Understanding why observability matters for AI agents
From the course: Creating Agents with Python and the AI Toolkit for Visual Studio Code
Understanding why observability matters for AI agents
When you're building AI agents, it's easy to focus on just getting the agent to respond. But here's the truth. Building an agent isn't just about what it says, it's about understanding why it said it, and how well it's performing behind the scenes. That's where observability comes in. Observability is about giving yourself visibility into an agent's inner workings. With the right traces, logs, and metrics, you can see how the agent makes decisions, which tool it calls, and where things might go wrong. Without that visibility, you're working without a clear view of what's happening inside the agent. Think about debugging. If your agent gives a wrong answer or fails to call the right tool, observability helps you trace the exact steps it took. Instead of guessing why it failed, you can pinpoint the issue, whether it's the prompt, the tool response, or something in your logic. It also plays a huge role in performance. With observability, you can measure latency, see which requests are…
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