tool-use

Prompting for a format gives a high success rate, and a high success rate is not a guarantee. This lesson locates the one place in the decoding loop where a guarantee is possible, shows what masking logits does to the probability distribution, works through why a 5 percent failure rate destroys tail latency rather than average latency, and separates the three families of structured output.

How LLM agents actually run: the iterative prompt-action-observation loop, the ReAct shape, the smallest tool-calling loop in twelve lines, why you always stack three termination layers, what the model sees on iteration N, and when a single prompt is the better answer.

A deep dive into designing tool schemas that LLMs actually call correctly — covering parameter naming, description quality, structured output via response schemas, output parsing, and error message design that drives self-correction.

Move beyond toy ReAct loops. Learn how to build a production-grade agent harness with a robust control loop, tool registry, schema validation, retry logic, token budgets, abort signals, and a persistent journal that survives crashes.

A capability-by-capability tour of frontier LLMs in 2026 — which models are strong at reasoning, code, math, long-context, multilingual, multimodal, and tool use, with hedged comparisons instead of point-estimate benchmark wars.

What separates an agent from a plain chatbot, the perceive-think-act loop they all share, and how to design one that doesn't loop forever or burn through your token budget.