Agents That Actually Work: The Rise of Tool-Use Models
The agent hype cycle is cooling into something useful: models natively trained to call tools, verify results and retry.
From prompting to tool-use training
The first wave of "agents" was just chains of prompts glued together with brittle scaffolding. The second wave trains tool-calling directly into the model, with rejection sampling on real API traces.
The result: fewer hallucinations, better recovery from errors, and workflows that survive a failed HTTP call without a human babysitting them.
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The agent hype cycle is cooling into something useful: models natively trained to call tools, verify results and retry.
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