To build a multi-step Gemini agent, let the model propose a function call, have your application validate and execute it, return the result with the matching call ID, and ask Gemini what to do next. Repeat until Gemini returns a response without another function call. A function declaration describes an action; it does not run your code.
What makes a Gemini integration an agent rather than just a chatbot?
A chatbot can answer from the conversation. An agent can also request actions through tools and use their results to decide what to do next. With Gemini custom function calling, the model selects and parameterizes a proposed action, but your application owns execution.
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Google describes the function-calling cycle as defining a function declaration, sending it with a model request, executing the requested code in your application, and returning the result so the model can produce a user-friendly response. The cycle can repeat across turns, including sequences in which one result informs a later call. See Google’s function-calling guide.
For example, if someone asks for the weather near a landmark, Gemini might first request a location lookup. Your application runs that lookup and returns the coordinates. Gemini can then request a weather lookup using those coordinates, and your application returns that result. The model can then explain the forecast. Each step is a handoff between Gemini and code you control.
What happens in each function-calling step?
- Declare the available functions. Provide each function’s name, purpose, and argument schema. This tells Gemini what it may request and what arguments to supply; it does not grant access to the function implementation.
- Send the user request and declarations to Gemini. The model may respond with ordinary text, one function call, or multiple function calls.
- Inspect and dispatch the calls. Your application checks the requested function names, validates arguments, and runs only functions it recognizes and permits.
- Return each result to Gemini. Package the function output with the matching call ID and function name, so the model can associate the result with the request that produced it.
- Continue or finish. Send the result back in the next model interaction. If Gemini requests another function, handle it and repeat. If there are no more function calls, present the model’s final response to the user.
Google’s tools overview describes custom function calls as structured requests containing a name, arguments, and unique ID. Your application executes the function and returns its result under the same ID; Gemini can then answer or request another tool call. See Using tools with Gemini API.
How should the application handle dependent steps?
Keep the loop explicit. The following language-neutral outline shows the responsibilities without tying the design to a particular SDK version:
- Define an application-side map from approved function names to implementations, such as
find_locationandget_weather. - Send the user’s request and function declarations to Gemini.
- For every returned step, check whether it contains function calls. For each call, verify that its name is in the approved map and validate its arguments against the expected schema and your application’s rules.
- Run the permitted function. Capture either a well-formed result or a controlled error outcome.
- Create a function-result step containing the returned call ID, function name, and result. Send it back to Gemini with the required conversation context.
- Repeat the call check and dispatch process, subject to a maximum turn or action limit. When a response contains no function calls, return its user-facing text.
In the location-and-weather example, the weather function should receive the location result from the earlier call—not a location guessed by the application or silently inferred from unvalidated model text. This is what makes the sequence compositional: the result of one executed function becomes context for the next model decision.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsHow do you preserve conversation state?
Gemini’s documented patterns include stateful interactions chained by a prior interaction ID and stateless interactions where the client resends the conversation history. Choose based on how your application is designed to retain and manage context.
| Approach | What the client sends | Context handling | Application control |
|---|---|---|---|
| Stateful | The initial user input or subsequent function results, together with the prior interaction ID as required by the API pattern. | Interactions chain using the previous interaction ID. | The application tracks the interaction ID and controls its own persistence of related application data. |
| Stateless | The complete conversation history on each request. | Resend the initial user input, every model-generated step exactly as returned, and the function-result steps. | The client explicitly retains and supplies the history. |
Do not rebuild prior model steps from memory or keep only the latest function result in stateless mode: Google’s guide specifies resending the full interaction history, including the earlier model-generated steps exactly as received. The stateful example instead chains requests with the previous interaction ID. Consult the current function-calling guide for the SDK-specific request structure.
What do function-calling modes control?
Google documents four function-choice modes: auto (the default), any, none, and validated. These settings constrain whether or how Gemini selects functions or shapes arguments. They do not execute application code, authorize a user, or make a requested action safe. Your application still has to inspect and handle every proposed call.
How are custom functions different from Gemini’s built-in tools?
| Tool type | Who executes it? | How the result returns | Important qualification |
|---|---|---|---|
| Custom function | Your application executes the requested function. | Your application returns the result associated with the matching call ID. | You implement dispatch, permissions, side-effect handling, and error behavior. |
| Built-in Gemini tool | Processing can be managed within the API interaction. | The built-in tool’s result is handled in the Gemini interaction flow. | Availability and behavior depend on the tool and supported model/API features. |
| Combined built-in and custom tools | Execution follows the path for each tool type. | Results become context for Gemini’s next step. | Google documents combining built-in and custom tools for the Gemini 3 series as a preview capability; check current support before relying on it. |
See Google’s tools overview for the documented distinction and current availability details.
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What safeguards should the application add?
The function-calling documentation explains the handoff pattern; it does not prescribe one production policy for every application. Treat the model’s function call as a request to evaluate, not as trusted authorization to act.
- Validate inputs. Check required fields, types, ranges, and resource identifiers before calling a service.
- Authorize actions independently. Confirm the user and application policy permit the requested operation. A valid function name or argument schema does not establish permission.
- Bound the loop. Set limits on turns, calls, execution time, and any resource use so unexpected repeated requests cannot run indefinitely.
- Handle failures deliberately. Define timeouts, retry rules, and clear error results. Avoid returning secrets or raw internal errors to the model.
- Make side effects safe. Use idempotency controls where retries could duplicate an operation, and require explicit user confirmation for consequential actions when appropriate.
- Return usable results. Give Gemini the relevant outcome in a clear, appropriately limited format, rather than an unfiltered service response.
What should you check before implementing?
Gemini model IDs, SDK syntax, preview labels, and feature availability can change. The official documentation describes implementation patterns rather than testing a particular application, so verify the current API behavior and supported features in Google’s linked guides before choosing a model or shipping a workflow.
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