Opening the black box: making agents transparent
1. Opening the black box: making agents transparent
We now have a fully working agent, one that can choose tools and pull in real-world data to answer a user’s question.2. From LLMs to Agents
But if we take a closer look at the agent we’ve built so far, we’ll notice a big gap: we can’t actually see what’s happening inside the agent when it generates a response. The lack of visibility is a real problem — especially when we're trying to build a reliable system such as our healthcare agent. Let’s say we asked the agent a question and it gave us a helpful response.3. Lack of visibility
But we have no idea if that answer was based on a tool call or just the model making something up. There’s no way to tell whether it actually used the search tool we gave it or hallucinated an answer instead.4. Lack of visibility
This makes it really difficult to evaluate the correctness of the response, especially in cases where accuracy and trustworthiness are critical.5. Debugging
Another major challenge is debugging. If the agent gives an incorrect answer, what exactly went wrong? Was the wrong tool used? Was the right tool used with the wrong parameters? Or did the tool return something useful but the model just ignored it? Right now, with the default agent behavior, we don’t know. We’re flying blind.6. Transparent agents
To build robust, testable, and trustworthy agents, we need transparency.7. Transparent agents
We need to see when the agent decides to use a tool, the input it sends, the output it gets back8. Transparent agents
and how that result influences the final answer. This is what turns agent development from guesswork into an engineering process. In the next exercise, we’ll address this head-on. We’ll customize our agent to trace tool usage and inspect intermediate steps in real time during development. This kind of tracing is essential when we’re building and debugging our agent.9. Let's practice!
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