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I Built an AI Agent With Real Tools. These 8 Things Surprised Me
Connecting an LLM to real tools changes everything. The hard part is no longer getting an answer. It is making sure the agent does the right thing.
Building an AI agent sounds straightforward when you first look at the architecture.
You connect a language model to a few tools, describe what each tool does, and let the model decide when to call them. One tool reads database records. Another searches documents. A third calls an API. The model chooses the next action based on the user’s request and the results it receives.
The first successful run can feel almost magical.
Ask the agent to find a customer, inspect an order, and summarize what happened. It selects the tools, processes the results, and returns a useful answer without requiring you to hardcode every step.
But the interesting engineering problems begin when you move beyond that first successful run.
A tool can return incomplete data. An API can time out after completing an operation. A model can repeat a request that has already succeeded. A seemingly harmless instruction can lead to an expensive or unauthorized action.
