How AI Agents Use Tools to Complete a Task

An AI agent is software that uses a model to choose steps toward an assigned goal. It can call tools to search records, run a calculation or change something in another application, then use the returned information to decide what to do next. Businesses considering an agent need to specify both the job and the actions the software is allowed to take.

For example, a proposed research assistant could search approved sources, compare what it finds and prepare a report with links. A purchasing assistant might also be able to place an order. Those applications require different permissions even if they use the same underlying model.

How an agent differs from a fixed sequence

In its December 2024 guide, Anthropic distinguishes workflows with predefined paths from agents that direct their own tool use. A fixed workflow can still contain an AI step, such as classifying a request before sending it to a predetermined queue. An agent can choose another search or tool after inspecting an intermediate result.

That flexibility adds decisions to test. Define what completion means, when the system should ask for help and when it must stop. Set limits on elapsed time, spending or tool calls where appropriate, so an unresolved task cannot continue indefinitely.

Separate information access from permission to act

A connected tool gives an agent a specific capability. Reading a file, sending an email and changing a customer record are separate capabilities. Ask the developer or supplier to show which ones are enabled and which accounts grant access.

For an initial research trial, read access and a place to save drafts may be enough. If the assistant later needs to send a report, specify permitted recipients and the approval required. A general instruction to be careful gives an administrator no concrete rule to enforce.

Documents and websites also introduce a security risk. OWASP’s 2025 guidance on prompt injection explains how instructions embedded in outside material can influence a model’s behavior. Limiting access and requiring approval for consequential actions can reduce the harm if an agent follows such instructions.

Write a task brief that can be checked

Describe the requested output, allowed sources, deadline and constraints. State what the agent should do when information is missing. For a supplier comparison, require source links and distinguish quoted prices from estimates; prohibit contacting suppliers or making purchases unless those actions are part of the assignment.

An illustrative brief could ask for three suppliers that deliver to a specified location, accept a stated order size and publish relevant product specifications. The output would include supporting links and unresolved questions. The agent would save a draft and stop if current pricing required contacting a salesperson.

During review, check the report against its sources. Ask the developer to make tool logs and saved changes available so a reviewer can see which files the agent accessed and which actions it took. Test this review process before granting access to live company systems.

Run a trial with recoverable tasks

Use a test environment or copies of records for actions that change data. Include missing documents, a failed search, conflicting instructions in source material and a tool returning an error. Check whether the application stops, asks for help or continues using an unsupported assumption.

Measure time to an acceptable completed task, including staff review and correction. Record usage costs and unfinished attempts. Compare those results with the existing process before deciding whether the extra flexibility warrants the expense.

Keep the initial design small enough to inspect. Several agents can divide a task, but they also exchange information and can duplicate effort or pass mistakes onward. Add another agent only for an identified role, with a way to measure whether that arrangement improves completion or quality.

At the end of the trial, retain the task brief, outputs, action logs and reviewer corrections. Use the failed cases when testing the next version, including any change to connected tools or permissions.