tool use

ReAct is the pattern behind most tool-using agents. What matters is why interleaving reasoning with actions (and observing results) beats reasoning alone or acting alone. Here is the answer that ties the prompt pattern to real agent behavior.

The model's training cutoff is frozen, but your dependencies shift weekly. Retraining is the wrong reflex. Here is how teams keep code suggestions current without touching the weights.

Generation has to end somewhere, and the wrong stop rule either truncates answers or wastes tokens on trailing garbage. Here is how the EOS token, stop strings, and max-token caps actually interact.

Agents get reached for too often. A strong answer pushes past the hype: most tasks are better served by a single structured call, and agents pay off only under particular conditions. Here is when one is worth it and how to stop it from compounding errors.

Agents and integrations depend on the model returning valid, schema-conforming output. The signal is stacking constrained decoding, schema validation, and retries, rather than trusting a prompt to do the job. Here is the production-reliability answer.

Agents fail in the middle, not only the end, so final-answer-only scoring conceals the real problems and rewards lucky paths. The signal is evaluating the whole trajectory and pinpointing where it broke. Here is the answer.

Real tools time out, rate-limit, and hand back malformed JSON. An agent that assumes every call succeeds is a demo, not a product. Here is the error-handling layer that keeps it running in production.

The CRM says the balance is $0 and the ledger says $412. An agent left to its own devices will average them into something confident and wrong. Designing the precedence before the model has to guess is the whole answer.

The agent loop assumes an observation is a short string, and images break that assumption in both directions. The signal is a context policy that keeps pixels out of the transcript and a verification step for the files the agent produces.

Auto-resolving support tickets means grounding answers in your knowledge base, safely calling real APIs (refunds, account changes), and knowing when to hand off to a human. Learn the agent loop, the guardrails, and how to measure resolution without eroding trust.

Agents that loop for minutes, invoke tools, and spawn sub-tasks demand durable execution rather than one request handler. See how to checkpoint state, recover after failures, cap runaway loops, and trace every move a non-deterministic agent makes.