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Reliable tool use starts with a clear runtime contract: the model requests an operation, but your application or the provider executes it, checks the result, and decides what happens next. In production, choose who owns that loop deliberately, validate data at tool boundaries, and require approval before sensitive side effects.
How AI agents use tools
A model does not execute an application tool by itself. The system describes available operations and their input shapes; the model emits a structured request; a runtime executes the request and returns a result. The model can then use that result to decide what to do next. Anthropic’s tool-use documentation summarizes the boundary plainly: “The model never executes anything on its own.”
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That distinction is more than terminology. The component that executes a call is where authorization, timeouts, retries, result validation, and execution logs must be handled. If the application owns execution, it also owns continuation: it must interpret whether the model requested a tool, whether a call succeeded, and whether the run should continue or stop. Provider-managed tools can shift some of that work to the provider, but the available modes and exact behavior depend on the platform.
The Tool Desk
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- Describe the operation. Provide the model with the tool’s purpose and an input schema that constrains the arguments it may request.
- Receive a structured request. Treat it as proposed input, not as proof that the call is permitted or safe.
- Authorize and execute. The application or provider checks applicable policy and permissions, then invokes the tool.
- Validate and return the result. Check the output before making it available to the model or using it elsewhere.
- Continue or finish. The runtime interprets the model’s next response and either handles another requested operation or returns a final result.
Anthropic describes a client loop that continues when the model indicates tool use and stops on other terminal reasons; its documentation also notes that server tools may perform multiple iterations within one request. These are vendor-described patterns, not a universal protocol. Make your own stop, retry, and error behavior explicit rather than assuming every provider or tool follows the same loop.
#1 Best Overall
Choose direct calls or programmatic orchestration
Direct tool calls and programmatic tool calling solve different control-flow problems. A direct call keeps the model involved in choosing the next operation. Programmatic orchestration uses ordinary code to carry out predictable processing, then returns useful results to the model. OpenAI’s Programmatic Tool Calling documentation describes the latter approach; the choice should follow the shape of the task, not a blanket expectation about speed or accuracy.
| Pattern | Best fit | What to define | Key trade-off |
|---|---|---|---|
| Direct tool call | A single lookup or action; a next step that depends on fresh model judgment; or a workflow where approval or preserving citations matters. | Tool purpose, argument schema, permission checks, execution behavior, and how results or errors return to the model. | The model can adapt between calls, but the application must manage the execution and continuation loop when tools are client-executed. |
| Programmatic tool calling | Predictable sequences where code can filter, join, rank, deduplicate, aggregate, or validate structured results. | Eligible tools and schemas, bounded processing stages, and what happens when a stage fails; return a compact, useful result to the model. | Code handles deterministic processing, but the eligible operations and failure behavior must be bounded and clearly specified. |
| Provider- or server-executed tools | A platform mode where shifting some execution responsibility to the provider fits the application. | Which party authorizes, executes, retries, times out, validates, logs, and continues; confirm the platform’s actual tool modes. | Execution responsibilities may shift, but the exact loop and controls vary by platform. |
Use a direct call when the model needs to reassess after each result or when a person must approve a particular action. Use programmatic orchestration when the intervening steps are predictable and code can reduce many raw results to a smaller structured answer. If neither pattern alone fits, use code for bounded data processing and return control to the model at decisions that genuinely require judgment.
Put checks where they can prevent harm
Separate automatic checks from human decisions. Input checks can reject or sanitize a request; tool checks can validate arguments and results; output checks can inspect what leaves the system. OpenAI’s guardrails and human review documentation draws the distinction directly: “Use guardrails for automatic checks and human review for approval decisions.”
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Validate at the action boundary
For each tool, validate that requested arguments fit its schema and the caller’s permissions. Validate returned data before it is passed to the model or used to trigger another operation. Place policy checks at the tool that creates the side effect—such as changing data or initiating an external action—rather than relying only on an earlier or later general-purpose check.
This placement matters in nested workflows. OpenAI notes that input guardrails run only for the first agent, output guardrails only for the final-output agent, and tool guardrails only for tools to which they are attached. A check elsewhere in a multi-agent flow does not automatically protect a tool that has no check of its own.
Pause sensitive actions for approval
When a call needs a human decision, stop before execution. The OpenAI Agents SDK documentation describes a resumable pattern: record the interruption and pending item, return state for review, approve or reject the item, and continue the same run. Design the approval record so the reviewer can identify the proposed action and relevant context; after rejection, ensure the pending action is not executed.
Approval is a control over a specific side effect, not a substitute for validating inputs or limiting permissions. Keep credentials and tool access no broader than the task requires; this is a prudent implementation practice alongside the documented checks, not a guarantee against mistakes.
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Prompt injection occurs when user input or retrieved content includes instructions intended to override the agent’s rules. If the agent treats that content as trusted direction, a downstream tool might expose private data or take an unintended action. OpenAI’s agent safety guide recommends keeping untrusted variables out of developer instructions, using structured outputs to constrain data flow, providing clear policy guidance and examples, enabling approvals for MCP actions, using input guardrails, and evaluating traces.
Best Value
Use these controls together: narrowly scoped tool operations and credentials, validated schemas and results, policy checks at side-effect boundaries, human review for sensitive calls, and trace-level monitoring. No single mitigation eliminates prompt-injection risk; the guide cautions that agents can still make mistakes or be tricked. Treat retrieved text as data to inspect, not as authority to change the system’s instructions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide who owns execution and recovery
Before choosing an implementation, make the ownership boundary explicit. The relevant questions are who runs each tool and continues the loop, whether the next step needs new model judgment or predictable code, what data and permissions each call can access, whether it has side effects, how schemas and results are validated, and how a run can be traced or resumed after interruption.
- Execution and continuation: Know which component calls the tool and interprets the model’s next response.
- Judgment versus deterministic work: Keep fresh model decisions at the model boundary; use ordinary code for predictable transformations.
- Data and authorization: Limit the data passed to a tool and the permissions available to it.
- Side effects and approval: Identify actions that require a human decision before execution.
- Validation and traceability: Check arguments and results, and preserve enough execution state to diagnose failures or resume reviewed work.
Do not assume one execution mode is universally faster, cheaper, or more reliable. Those outcomes depend on the workload and the complete system; measure them in the environment where the agent will run.
What autonomy data can—and cannot—tell you
Anthropic’s February 18, 2026 article, “Measuring AI agent autonomy in practice”, analyzed millions of human-agent interactions across Claude Code and Anthropic’s public API using the company’s privacy-preserving measurement approach. The findings describe those products and that methodology; they do not establish universal adoption, performance, or safety across agent deployments.
- Anthropic reported that its longest-running Claude Code sessions increased from under 25 minutes to over 45 minutes in three months.
- About 20% of new-user sessions used full auto-approval, compared with over 40% among experienced users.
- Software engineering accounted for nearly 50% of agentic activity on Anthropic’s public API.
- On the most complex tasks, Claude Code asked for clarification more than twice as often as humans interrupted it.
Anthropic also said, “Most agent actions on our public API are low-risk and reversible.” That observation is limited to the company’s public-API analysis; it should not be generalized to other organizations’ tools or risk profiles. The article describes measuring agents as difficult and presents its analysis as an early step. For system design, the practical implication is to treat autonomy as a question about workload, permissions, reversibility, and user practice—not merely a model setting.
Quick Recap
Production readiness checklist
- Document which runtime executes each tool and owns continuation.
- Give every operation a clear purpose and constrained input schema.
- Set explicit timeout, retry, error, and stop behavior for the execution path.
- Validate tool arguments and results, and check permissions at the action boundary.
- Use code for bounded, predictable processing; return compact structured results to the model.
- Require approval before sensitive side effects and preserve state needed to resume or reject the pending action.
- Keep retrieved content separate from trusted instructions and monitor traces for failures or unexpected behavior.
- Measure reliability, latency, and cost for the actual workload instead of adopting a universal threshold.
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