Flutter AI Agents: Building Autonomous Workflows in Mobile Apps (With Code Samples)

December 26, 2025December 26, 2025 Shaiq Khan Featured

Artificial Intelligence in mobile apps is rapidly evolving—from simple chatbots and recommendation engines to autonomous AI agents that can reason, plan, and act with minimal human intervention. Building autonomous AI agents in Flutter has evolved in 2025 into a sophisticated practice of orchestrating “agentic” workflows—systems capable of independent reasoning and taking real-world actions. This shift is supported by high-level SDKs like Google’s Vertex AI SDK for Firebase and specialized Flutter-native toolkits.

In this article, we’ll explore AI agents in Flutter, how they differ from traditional AI features, and how to build autonomous workflows inside Flutter apps using modern LLMs, background tasks, and tool execution.

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Feature	Traditional AI	AI Agents
Response	Single output	Multi-step actions
Memory	Stateless	Long-term / short-term
Tools	None or limited	API calls, DB, system tools
Autonomy	Low	High
Use cases	Chat, search	Task automation, decision making

The Brain (LLM): Models such as Gemini 1.5 Pro or Flash provide reasoning logic.
The Hands (Tools/Function Calling): Dart functions allow the agent to interact with the outside world. These can be APIs, databases, or device features like GPS.
The Memory: Persistent state that tracks conversation history and progress through complex workflows.
The Sensors (Multimodal Input): The ability to process images, audio, and sensor data as context for actions.

Setting Up the Ecosystem

To build these agents, the 2025 Flutter ecosystem uses the Google AI Dart SDK and Firebase Vertex AI SDK.

Key Dependencies:

yaml

dependencies:
  firebase_core: ^3.0.0
  firebase_vertexai: ^1.1.0 # Standard for agentic logic in 2025
  riverpod: ^2.5.0          # For managing agent state

Implementing Autonomous Workflows

The core of an agent is Function Calling. This allows the LLM to request the execution of a specific Dart function when it determines that a tool is needed to fulfill a user’s goal.

Code Demo: A Travel Booking Agent

In this example, the agent can autonomously check flight availability and book tickets.

Step 1: Define the Tools

final bookingTools = Tool(
  functionDeclarations: [
    FunctionDeclaration(
      'checkFlights',
      'Searches for flights between two cities on a specific date',
      Schema.object(properties: {
        'origin': Schema.string(description: 'Departure city'),
        'destination': Schema.string(description: 'Arrival city'),
        'date': Schema.string(description: 'Flight date in YYYY-MM-DD format'),
      }),
    ),
    FunctionDeclaration(
      'bookTicket',
      'Confirms a booking for a specific flight ID',
      Schema.object(properties: {
        'flightId': Schema.string(description: 'The unique ID of the flight'),
      }),
    ),
  ],
);

Step 2: Initialize the Agent with Reasoning Logic

final model = FirebaseVertexAI.instance.generativeModel(
  model: 'gemini-1.5-flash',
  tools: [bookingTools],
  systemInstruction: Content.system(
    'You are a travel agent. First check availability. '
    'Ask for confirmation before booking any flight.'
  ),
);

Step 3: The Autonomous Loop

The agent returns a functionCall when it needs to “act”.

Future<void> processAgentStep(String userInput) async {
  final chat = model.startChat();
  var response = await chat.sendMessage(Content.text(userInput));

  // The Agent decides which tool to call
  for (final call in response.functionCalls) {
    if (call.name == 'checkFlights') {
      // 1. App executes the real API call
      final results = await myApiService.search(call.args['origin'], call.args['destination']);
      
      // 2. Feed the real-world result back to the agent
      response = await chat.sendMessage(
        Content.functionResponse('checkFlights', {'flights': results})
      );
      
      // 3. Agent now reasons over the results to answer the user
      print(response.text); 
    }
  }
}

Project Structure (Clean Architecture)

lib/
 ├── data/
 │    ├── agents/
 │    ├── services/
 │    └── repositories/
 ├── domain/
 │    ├── entities/
 │    ├── usecases/
 │    └── agents/
 ├── presentation/
 │    ├── bloc/
 │    └── ui/

Defining an Agent Interface

abstract class AIAgent {
  Future<void> perceive();
  Future<void> reason();
  Future<void> plan();
  Future<void> execute();
}

Task Automation Agent (Example)

Entity

class Task {
  final String id;
  final String title;
  final DateTime dueDate;
  int priority;

  Task({
    required this.id,
    required this.title,
    required this.dueDate,
    this.priority = 1,
  });
}

Agent Implementation

class TaskAutomationAgent implements AIAgent {
  final TaskRepository repository;
  List<Task> tasks = [];
  List<Task> overdueTasks = [];

  TaskAutomationAgent(this.repository);

  @override
  Future<void> perceive() async {
    tasks = await repository.getTasks();
  }

  @override
  Future<void> reason() async {
    overdueTasks = tasks
        .where((t) => t.dueDate.isBefore(DateTime.now()))
        .toList();
  }

  @override
  Future<void> plan() async {
    for (var task in overdueTasks) {
      task.priority = 5;
    }
  }

  @override
  Future<void> execute() async {
    for (var task in overdueTasks) {
      await repository.updateTask(task);
    }
  }
}

Running the Agent

final agent = TaskAutomationAgent(taskRepository);

await agent.perceive();
await agent.reason();
await agent.plan();
await agent.execute();

This is a fully autonomous AI agent.

Adding LLM-Based Reasoning

LLM Service

class LLMService {
  Future<String> analyzeTasks(List<Task> tasks) async {
    // Call LLM API
    return "Increase priority for overdue tasks";
  }
}

Enhanced Reasoning

@override
Future<void> reason() async {
  final response = await llmService.analyzeTasks(tasks);
  if (response.contains("Increase")) {
    overdueTasks = tasks
        .where((t) => t.dueDate.isBefore(DateTime.now()))
        .toList();
  }
}

Agent with Tool Usage

AI agents often use tools.

Tool Interface

abstract class AgentTool {
  Future<void> run(Map<String, dynamic> input);
}

Notification Tool

class NotificationTool implements AgentTool {
  @override
  Future<void> run(Map<String, dynamic> input) async {
    // Send local notification
  }
}

Tool Execution

await notificationTool.run({
  "title": "Overdue Tasks",
  "count": overdueTasks.length
});

Multi-Agent System in Flutter

You can run multiple agents.

TaskAgent → NotificationAgent → AnalyticsAgent

Agent Manager

class AgentManager {
  final List<AIAgent> agents;

  AgentManager(this.agents);

  Future<void> runAll() async {
    for (final agent in agents) {
      await agent.perceive();
      await agent.reason();
      await agent.plan();
      await agent.execute();
    }
  }
}

Background Execution

Use:

workmanager
android_alarm_manager_plus
background_fetch

Workmanager().executeTask((task, input) async {
  await agentManager.runAll();
  return true;
});

Performance Optimization

Use isolates for heavy reasoning
Cache LLM responses
Debounce agent runs
Limit token size
Avoid UI thread blocking

Security Considerations

Never store API keys in app
Use secure backend proxy
Encrypt agent memory
Validate agent actions
Rate-limit workflows

Testing AI Agents

Unit Test

test('Agent marks overdue tasks', () async {
  final agent = TaskAutomationAgent(fakeRepo);
  await agent.perceive();
  await agent.reason();
  expect(agent.overdueTasks.isNotEmpty, true);
});

Conclusion:

In the article, I have explained Flutter AI Agents: Building Autonomous Workflows in Mobile Apps. This was a small introduction to User Interaction from my side, and it’s working using Flutter. AI agents are not the future—they’re the present.

With Flutter, you can build:

Autonomous workflows
Intelligent decision systems
Scalable agent architectures
Privacy-friendly AI apps

By combining clean architecture, LLMs, and tool-driven execution, Flutter developers can create apps that don’t just respond—but act.

❤ ❤ Thanks for reading this article ❤❤

If I need to correct something? Let me know in the comments. I would love to improve.

Clap 👏 If this article helps you.

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