Your AI can call tools. Now make it reliable.

The first successful tool call feels like a big step. Your app gets a question, the model asks to search, and a real answer comes back. A conversation has turned into something that can do work.

The next interesting moment is usually less pleasant. A lookup times out. The model sends the wrong identifier. Someone clicks twice. An action succeeds, but the response disappears before your application receives it.

Those moments deserve as much design attention as the successful call.

Give each tool one understandable job

Start with tools you can describe in a sentence. “Look up an order by its ID” is easier to reason about than “manage the customer's account.” A narrow operation gives the model a clearer choice and gives your application a smaller surface to validate.

Describe what the tool does, which inputs it needs, and what its result means. Distinguish reading information from changing it. Finding a draft and publishing it are separate operations, even if they sit next to each other in your code.

Anthropic's tool-use documentation explains the separation between a model requesting a client tool and your application executing it. That distinction is useful beyond any one provider: a requested action still needs your application's rules.

Treat the arguments as a request

A well-formed argument object can still contain the wrong values. Check types, allowed values, required fields and ownership before running the operation. If a request needs an order ID and the conversation contains two orders, guessing isn't a convenient shortcut. It is a different decision with real consequences.

Keep authorization in ordinary application code. The model can propose looking up a record; the authenticated user's permissions determine whether that record is accessible. A sentence in a tool description is useful guidance, but it isn't an access control.

When information is missing, return an explicit error or ask for the detail. Don't turn a failed lookup into an empty successful result. Otherwise the model may confidently explain that nothing exists when the search never worked.

Decide what happens after a timeout

Consider an app that creates a support ticket. The service saves the ticket, but the connection drops before the response arrives. From your application's point of view, the outcome is uncertain. Creating another ticket immediately can leave the customer with two.

For operations that support idempotency, persist an operation key before submission and reuse it according to the service's documented contract. Record the service's request identifier when it is available. If the outcome is uncertain, inspect that operation before deciding whether another submission is safe.

Read-only calls are usually easier to retry, but even those need a deadline and an attempt limit. A search loop that never finishes is still a broken user experience.

Put a boundary around the whole job

One user request can turn into several model and tool calls. Decide how much work the application will allow before you hand over control.

Useful boundaries include:

• A deadline for the full request, including tool execution.
• A limit on tool calls and repeated attempts.
• A spending budget where operations incur charges.
• A clear confirmation step for actions that require the user's approval.

Apply those boundaries in your execution code. Telling a model to “be efficient” doesn't stop a loop. When a limit is reached, tell the person what completed and what still needs attention. “The search finished; publishing is waiting for approval” is much more useful than a generic success message.

Keep enough history to explain the result

You should be able to answer a few questions after a run: which operation was requested, which inputs passed validation, what the service returned, and how the app reached its final state.

That doesn't mean logging every prompt, secret or customer document. Keep the minimum useful record, redact sensitive fields, and control who can read it. Correlation IDs and explicit operation states often explain more than a huge unstructured transcript.

Use that record when checking failure cases. Try a missing argument, an unauthorized record, a slow response and a duplicate submission in an isolated environment. Verify the result in the underlying service as well as the text shown to the user.

Make the second attempt predictable

A reliable tool-using app gives people a way to recover. They can see whether the job finished, understand what failed, and continue without accidentally repeating a completed action.

That work is less dramatic than the first successful tool call. It's also what makes the feature comfortable to use twice.

Browse the tool catalog for available capabilities, or read how to compare models for your own tasks. If the work needs a machine of its own, plan the GPU job first.