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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsTo build a multi-step Gemini agent, your application must run the functions Gemini requests, return their results, and send the conversation back for the next model turn. A function declaration describes a possible action; it does not execute your code. The agent is the repeated handoff between Gemini and your application—not a chatbot that merely describes what it would do.
What makes this a multi-step agent?
Consider a request such as, “Find the weather in the city where the conference is being held.” The application may need to look up the conference location, then query a weather service for that location. The second action depends on the first result, so the model needs another turn after your application performs the lookup.
The flow is: user request → Gemini function call → application execution → function result → another Gemini turn. Gemini can then request another function or provide a final response. Google’s function-calling guide describes this as a cycle that can repeat across turns and supports both multiple calls in one turn and sequential, compositional calls.
What Gemini does—and what your application must do
A function declaration gives Gemini a function’s name, purpose, and argument schema. The model can use that information to propose a call with structured arguments. It does not receive the ability to run your application’s function. Google puts the distinction plainly: “The model doesn’t execute the function itself. Extract the name and args and execute in your application.”
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- Gemini selects and parameterizes a proposed action. It returns a function name, arguments, and a unique call ID.
- Your application owns execution. It decides whether the requested function is recognized and permitted, runs the code, and handles failures or side effects.
- Your application returns the result. Include the matching call ID and function name so Gemini can associate the result with the request.
- Gemini continues the interaction. Given the function result and conversation context, it can request another function or formulate a user-facing answer.
This separation matters: model output is a proposed action, not authorization to carry it out. Your code controls what actually runs.
Build the request-to-result loop
Start by declaring the functions the application can actually perform. For the example, that might be find_conference_location, which accepts a conference name, and get_weather, which accepts a location. Give each function a clear description and define its arguments with a schema so the model can produce structured requests.
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- Send the user request and function declarations to Gemini. The model may return a normal response, one function call, or multiple calls.
- Inspect each returned step. If it contains function calls, look up each requested name in an application-owned function map. Execute only functions your application recognizes; do not treat an arbitrary model-provided name as executable code.
- Run the selected function with validated arguments. For a dependent task, execute the location lookup first. The next model turn can use that result to decide whether to request the weather lookup.
- Package each function result with its matching call ID and function name. The ID ties the result to the specific request, including when Gemini returned multiple calls.
- Send the function result or results back to Gemini with the required conversation context. Gemini can then return another function call or its final response.
- Repeat while Gemini requests functions. Stop when there are no more function calls and surface the model’s user-facing output.
The important implementation detail is the dispatch loop: examine what the model returned, execute the application function, associate the result with the correct call ID, then continue the interaction. A single function-call response is not the end of a multi-step workflow.
Choose how to preserve conversation state
Gemini needs context from earlier turns to make a dependent decision. Google’s guide shows two approaches: retain and resend the full conversation history, or chain interactions using the prior interaction ID in the stateful pattern.
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| Approach | What the client retains and sends | How context continues | Application control |
|---|---|---|---|
| Stateless | The complete interaction history: the initial user input, every earlier model step exactly as returned, and the function result step. | The client resends the history with each new request. | The client explicitly manages and supplies the history. |
| Stateful | The prior interaction ID and the new input, such as the function result. | The next interaction chains from the prior interaction ID. | The application tracks the interaction ID and controls what it sends in each step. |
Use the stateless pattern when your application needs to provide the conversation history on every request. Use the stateful pattern when chaining from the previous interaction ID fits your implementation. These are different context-management patterns; the documentation does not establish that one is inherently cheaper, faster, or more private.
Custom functions and built-in Gemini tools are different
With a custom function, Gemini returns a structured request and your application runs the code, then returns the result under the same call ID. Built-in tools follow a different execution path: processing can be managed within the API interaction. Google’s tools overview describes both paths.
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Google documents combining built-in and custom tools for Gemini 3 series as a preview capability. Preview status and support can change, so check the current tools documentation before relying on a combined setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Put safeguards around application execution
Function calling gives the model a way to request actions; it does not supply a production policy for deciding whether or how those actions should run. Design those controls in your application. Depending on the function and its consequences, consider:
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- Validate arguments against the declared schema and your application’s own constraints before execution.
- Authorize the action for the current user and session rather than treating the model’s request as permission.
- Bound the loop with a maximum number of turns so repeated calls cannot continue indefinitely.
- Set timeouts and retry rules for functions that depend on external services, and handle failures in a way the next model turn can interpret.
- Use idempotency where appropriate so a repeated request does not accidentally repeat a consequential operation.
- Require confirmation for consequential actions, such as sending, purchasing, deleting, or changing something on a user’s behalf.
These are application-design recommendations, not a single policy prescribed by the function-calling examples. The right checks depend on what your functions can do.
Understand function choice modes
Google documents the function choice modes auto (the default), any, none, and validated. They constrain function selection or argument shape. They do not execute application-owned custom code; the application still has to dispatch the request, run an authorized function, and return its result.
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