The interface and control flow through which an LLM proposes a tool action and an application validates, executes, and handles its result.
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12 articles in this tag.
Two permission grants land on the review table. One lets the agent read every ticket and send email. The other lets it read assigned tickets and draft…
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The moment you give an agent a tool, you stop deciding what it does and start deciding what it is allowed to do. The real design question is not "can the…
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The agent worked in the demo. Then you ran it on your own task, and it called a tool you never expected — or kept calling tools after the answer was…
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Your agent runs. It calls tools. It produces output. And yet, something is wrong—or it never stops running at all. The frustrating part is that you can't…
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Frameworks change. The model underneath them doesn't. Learn the mechanism first, and every new tool becomes just another wrapper around something you…
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That single distinction separates a demo from a system. When you chat with an LLM directly, you are the pipeline: you judge the output, check the facts,…
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When people first hear that an LLM can "use tools," they usually picture the model reaching out and doing something itself—searching the web, running code,…
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A tool call returns status: "ok". The pipeline moves on. Nobody notices that the result answers a slightly different question than the one the model asked.
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A proposed tool call sits on your screen: send the summary to the client. You have a policy document open in another tab. Is this allowed, does it need…
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Most beginners expect an agent to be a special kind of model—something with built-in magic that can browse the web, run code, and get things done. Then…
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Structured output and tool calling look almost identical from the outside. Both accept a JSON schema. Both return clean key-value pairs instead of…
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That gap is not bad luck. It is a missing model. Most beginners picture tool calling as a straight line: ask, call, answer. That picture is accurate for…
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