What Tool use (function calling) means in practice
Tool use is the LLM capability that turns a chatbot into an agent. The model emits a structured JSON call to a registered function ("get_account_balance(customer_id=...)"), the runtime executes the function, the result is appended to the conversation, and the model continues reasoning. The reliability hinges on three things: the schema for each tool (well-typed, narrowly scoped), the system prompt that tells the model when to use which tool, and the validation that catches malformed calls before they hit a real system. In regulated deployments every tool call passes through a permission layer and is logged with full provenance so a regulator can replay the decision chain.
Related terms
Agentic AI →
Systems where an LLM acts as a planner that chooses tools, decomposes tasks, and iterates toward a goal rather than producing a single completion.
ReAct (Reason + Act) →
An agent design pattern where the LLM alternates between explicit reasoning steps ("thought") and tool-using action steps ("action"), looping until the goal is met.
Guardrails →
Runtime checks that intercept LLM inputs and outputs to enforce policy — blocking PII leakage, prompt-injection attempts, off-topic queries, unsafe responses.
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Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.