Long-Horizon Agents: Why Multi-Turn Reasoning Breaks and the Practical Training Tricks That Fix It

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Almost every modern language model looks impressive on a two-step demo. You ask it to check a database or summarize a document, it calls the right tool, formats the answer, and looks like an autonomous engineer. The illusion falls apart the moment you ask that same model to complete a 30- or 50-step workflow: diagnosing a failing Kubernetes cluster, navigating a multi-file pull request, or running a 48-hour industrial simulation. Around step 10, the agent makes a small typo in a terminal command. By step 15, it misinterprets a confusing error message.

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