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An AI agent should not execute every function the model proposes. Keep those decisions separate: let the model request a tool, then have your application check whether the request fits the user’s intent, has the required information, and is appropriate to run. In a course-recommendation agent, that means answering “Hello” directly, looking up courses when asked “What courses do you have?”, and clarifying an incomplete enrollment request before calling an action that changes user state.
Why an agent should not call a tool for every message
In a course-recommendation project built with ASP.NET Core, Quoc Bao An Nguyen describes an initial design that forwarded model function calls to backend APIs even for a simple message such as “Hello.” That can add API activity and latency without helping answer the user, while leaving the application with little control over execution. The account describes adding an orchestration layer to decide what should happen next.
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The key distinction is between a tool request and a tool execution. A model can propose a function call, but the application receives that proposal and can route it, validate it, defer it, or decline to execute it. Treating execution as an application decision is the practical answer to “How do I stop my AI agent from calling tools for every message?”
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The example uses three backend functions: GetCourses(), ValidateUser(), and EnrollCourse(). The author says the functions were exposed with structured input and output schemas. A useful routing decision considers whether external data is needed, whether required parameters are present, whether the operation reads information or changes state, and what orchestration adds to latency and maintenance.
| User request | Likely route | Why |
|---|---|---|
| “Hello” | Answer directly; no backend call | A greeting does not require course data or an account action. |
| “What courses do you have?” | Call GetCourses() |
The answer depends on information held by the application. |
| “Enroll me in a backend course.” | Check for missing details and clarify before any enrollment action | The request may not identify a specific course or include other required parameters, and enrollment changes user state. |
These routes are examples, not universal rules. A greeting that asks about account status, for instance, may need a lookup; an action should not proceed merely because a tool was requested. The application should use the meaning and completeness of the actual request to choose the next step.
Defer state-changing actions until the request is complete
For enrollment, the described agent identifies missing information, asks a follow-up question, and calls the function only after it has the needed parameters. A robust flow separates clarification from execution:
- Interpret the user’s intent and determine whether the request is informational or action-oriented.
- Check the proposed function and its arguments against the operation’s required inputs and schema.
- If information is missing or ambiguous, ask a focused follow-up question rather than executing the function.
- Once the request is sufficiently specified, apply application-side validation and any authorization or confirmation rules appropriate to the action.
- Execute the function, then report the result based on the backend response.
Having complete parameters helps prevent actions on incomplete requests; it is not, by itself, a guarantee of safety. Authorization, business rules, and any confirmation requirement belong in the application’s control flow, not solely in the model’s prompt.
Use prompts, schemas, and orchestration together
The project account describes refining explicit tool-use rules, improving function descriptions, and carrying conversation context across turns. Those measures help the model propose relevant calls and preserve details gathered during clarification. Structured schemas make expected inputs and outputs clearer, but they do not replace application-side checks.
OpenAI documents three tool_choice modes for its API: none means the model will not call a tool and instead generates a message; auto lets it choose a message or one or more tool calls; and required requires one or more tool calls. The OpenAI function-tool definition also describes parameters using JSON Schema and includes a strict-validation setting. These are OpenAI API semantics, not guarantees shared by every provider or framework; check the current documentation for the API in use: OpenAI Responses API reference.
As an orchestration design, an application can also omit tool definitions or disable tools during an initial classification pass, then expose only the tools relevant to the next step. This is an implementation option, not a measured result or a provider-independent feature. Whichever mechanism is used, keep the application’s final execution checks explicit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate routing without overstating results
Nguyen reports trying simulated intent scenarios, multi-turn conversations, and incomplete or ambiguous edge cases. The account describes qualitative outcomes—fewer unnecessary calls, more consistent responses, and better handling of complex requests—but provides no numeric call-rate results, latency measurements, cost comparisons, traffic volumes, or reproducible test details. It is a small project account, so it supports describing the approach, not quantifying its effect.
A practical evaluation should check whether each scenario takes the intended route: a simple greeting without a call, a data-dependent question with the appropriate read call, and an underspecified action with clarification before execution. Include multi-turn tests to verify that answers from earlier turns are carried forward, and ambiguous cases to ensure they do not trigger an unintended action. Record tool selection, arguments, application decisions, and outcomes so a misroute can be traced to the model proposal, schema, prompt, or orchestration rule.
Trade-offs to plan for
- More control: The application can keep routine conversation away from APIs and gate actions on complete inputs.
- More flow complexity: Routing, clarification, validation, and execution create additional branches to design and maintain.
- Harder debugging: A failure may come from the prompt, function description, schema, context handling, or application policy, so logs should make each stage visible.
- Added orchestration work: A classification or clarification step can itself add processing and latency; the project account does not quantify that overhead.
The source account is Quoc Bao An Nguyen’s DEV Community article, “How I Built an AI Agent with LLM Function Calling (and Avoided Unnecessary Tool Calls)”. Its approach is best understood as a control-flow pattern: the model suggests, the application decides, and the backend runs only after the request passes the relevant checks.
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