Tool Use

Tool use connects a foundation model to capabilities outside its learned parameters. A tool might look up an order, calculate a tax amount, search documents, create a support ticket, or reserve inventory. The model receives descriptions of available tools and their expected inputs, then can request an appropriate operation. The application or agent runtime executes the operation and returns its result to the model, which can explain the outcome or request another operation.

A typical interaction begins with a user asking, “Where is my shipment?” The application provides a tool definition for retrieving shipment status, including a structured input schema. The model requests that tool with an order identifier. Application code verifies the identifier and the user's authorization, calls the shipping system, and passes the response back to the model. The model then produces an answer grounded in the returned status rather than guessing from its training data. Generating a tool request is not the same as executing it successfully.

Tool use is a building block of AI agents. An agent combines a model with instructions, tools, and orchestration logic to pursue a goal through potentially multiple steps. For example, a customer-service agent could retrieve an order, check refund eligibility, request approval, and submit an authorized refund. Other business applications include scheduling appointments, investigating operational incidents, reconciling invoices, and checking inventory before preparing a sales quote. A single tool call does not necessarily make an application an autonomous agent; agent behavior depends on how it selects and coordinates actions toward a goal.

In cloud applications, tools commonly run as serverless functions, containerized services, or authenticated API endpoints. Tool definitions should describe purpose, required parameters, acceptable values, and possible results clearly. Prefer narrowly scoped business operations, such as retrieving an authenticated customer's orders, over unrestricted database queries or arbitrary command execution. Keep credentials in the execution environment rather than placing them in prompts, and enforce identity and authorization outside the model.

Security boundaries are essential because a model's proposed arguments are untrusted input. Validate schemas and business rules, restrict network destinations, and apply least-privilege permissions. Retrieved content and tool responses can contain prompt-injection attempts, so they must not be allowed to redefine tool permissions or approval requirements. High-impact actions, such as transferring money or deleting resources, should require explicit approval enforced by the application. Generated confirmation text alone is not evidence of authorization.

Tool use introduces latency, inference charges, API costs, and additional failure modes. Set timeouts, limit the number of tool calls, handle unavailable dependencies, and use bounded retries. Write operations need idempotency controls to prevent repeated requests from creating duplicate refunds or reservations. Record tool selections, validated arguments, execution outcomes, and approvals with appropriate protection for sensitive data. Evaluate both task completion and whether the application chose safe, appropriate actions.

Tool use does not retrain the model, guarantee factual answers, or automatically grant access to enterprise systems. Retrieval-augmented generation supplies external information for generating answers; retrieval can itself be exposed as a tool, but tools also support calculations and state-changing operations. For predictable, fixed procedures, a deterministic workflow may be simpler and safer than allowing a model to choose every step. Use agentic selection where flexibility provides a clear business benefit, while retaining deterministic controls around execution.