• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
• AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

Two familiar agent failures, incompatible tool output types and degradation as the tool catalogue grows, get one answer here: wrap each tool in an LLM interface and retrieve tools at inference instead of listing schemas in the prompt.

• articleHarness Engineering for Predictable Agentic Systems: An Empirical Study of Deterministic Execution ConstraintsSaransh Dhage
• articleThe Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent BehaviorXiangzhe Xu, Hamidreza Saghir, Qianhui Wu, Marc-Alexandre C\^ot\'e, Tong Wang, Kiran Lakkaraju, Kexin Pei, Xiangyu Zhang
• articleToolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling AgentsYiShan Zheng, Yuan Wu, Yi Chang

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