Multi-Agent System with ADK: Building Collaborative AI

Overview:

In 2025, multi-agent systems are emerging as a transformative force in the field of Artificial Intelligence. Rather than relying solely on isolated intelligent agents, modern AI solutions increasingly depend on the power of collaboration where multiple agents operate simultaneously, communicate effectively, and work towards shared objectives. These multi-agent systems offer a dynamic framework for solving complex, real-world problems that are difficult for single agents to manage alone. From autonomous vehicles coordinating traffic flows to distributed AI teams handling logistics, multi-agent systems enable new levels of scalability, flexibility, and resilience.

To support this growing paradigm, the Agent Development Kit (ADK) provides a robust set of primitives specifically designed for composing and managing multi-agent systems. These primitives allow developers to build sophisticated collaborative architectures where agents can interact, negotiate, delegate tasks, and adapt in real time. Whether it’s implementing leader-follower structures, task allocation protocols, or consensus mechanisms, ADK enables the development of intelligent patterns that are foundational to successful multi-agent systems.

This comprehensive guide explores how ADK primitives can be used to implement several well-established patterns in multi-agent systems, from decentralized control to cooperative problem solving. By mastering these design patterns and understanding how ADK facilitates their execution, developers can unlock the full potential of multi-agent systems to create powerful, adaptive, and efficient AI-driven applications that meet the demands of 2025 and beyond.

Understanding ADK’s Role in Multi-Agent Patterns

The ADK provides a robust framework. It simplifies the design and implementation of complex multi-agent architectures. By offering core primitives like LlmAgent, SequentialAgent, ParallelAgent, and LoopAgent, alongside interaction and state management mechanisms, ADK allows developers to translate theoretical multi-agent patterns into practical, deployable solutions. These primitives are crucial for defining agent behaviors, managing interactions, and orchestrating sophisticated workflows within multi-agent systems.

Coordinator/Dispatcher Pattern with ADK Primitives

The Coordinator/Dispatcher Pattern is a fundamental approach for routing tasks to specialized agents.

  • Structure: This pattern features a central LlmAgent acting as a Coordinator. This Coordinator manages several specialized sub_agents. Each sub_agent is designed for a specific task or domain.
  • Goal: The primary objective is to intelligently route incoming user requests to the most appropriate specialist agent. This ensures efficient task handling and specialized responses.
  • ADK Primitives Used:
    • Hierarchy: The Coordinator lists its specialists directly within its sub_agents definition. This establishes a clear delegation hierarchy.
    • Interaction: This pattern primarily leverages LLM-Driven Delegation. The Coordinator’s underlying Large Language Model (LLM) makes decisions. These decisions rely on clear descriptions for each sub_agent and precise instruction given to the Coordinator. Alternatively, Explicit Invocation (AgentTool) can be used. This occurs if the Coordinator includes AgentTool-wrapped specialists in its tools list. This allows for more direct invocation based on the LLM’s reasoning.

Sequential Pipeline Pattern with ADK Primitives

The Sequential Pipeline Pattern is ideal for multi-step processes where tasks must be executed in a specific order.

  • Structure: A SequentialAgent contains sub_agents. These execute one after another in a fixed, predefined sequence.
  • Goal: This pattern implements a multi-step process. The output generated by one agent (or step) automatically feeds as input into the subsequent agent. This ensures a controlled and ordered workflow.
  • ADK Primitives Used:
    • Workflow: The SequentialAgent explicitly defines and enforces its sub-agents’ execution order. This makes it a perfect primitive for this pattern.
    • Communication: Communication between agents in a sequential pipeline primarily utilizes Shared Session State. Earlier agents write their processing results (often using output_key) to the shared state. Later agents then read those results directly from context.state to inform their own operations. This allows for seamless data flow.

Parallel Fan-Out/Gather Pattern with ADK Primitives

The Parallel Fan-Out/Gather Pattern boosts efficiency by performing independent tasks concurrently.

  • Structure: This pattern uses a ParallelAgent to run multiple sub_agents simultaneously. Often, this parallel execution is followed by a subsequent agent (typically within a SequentialAgent). This agent then aggregates the results from the concurrently running agents.
  • Goal: The aim is to execute independent tasks in parallel to significantly reduce overall latency. Then, it collects and combines their individual outputs into a unified result. This is highly effective for tasks like fetching data from multiple sources.
  • ADK Primitives Used:
    • Workflow: The ParallelAgent is the core primitive for enabling concurrent execution (the “Fan-Out” phase). This ParallelAgent is frequently nested within a SequentialAgent. This manages the overall flow. Specifically, it ensures that the subsequent aggregation step (the “Gather” phase) only begins after all parallel tasks complete.
    • Communication: Sub-agents in this pattern write their respective results to distinct keys within the Shared Session State. The subsequent “Gather” agent then reads multiple state keys. It retrieves all necessary data from the concurrent operations for aggregation.

Hierarchical Task Decomposition with ADK Primitives

The Hierarchical Task Decomposition pattern is vital for tackling complex problems by breaking them down.

  • Structure: This involves a multi-level tree structure of agents. Higher-level agents break down broad, complex goals into smaller, more manageable sub-tasks. They then delegate these sub-tasks to lower-level, specialized agents.
  • Goal: The primary objective is to efficiently solve complex problems. This happens by recursively decomposing them into simpler, more granular steps. Individual agents can competently execute these. This mirrors how humans often approach large projects.
  • ADK Primitives Used:
    • Hierarchy: The pattern naturally builds using a multi-level parent_agent/sub_agents structure. This allows for clear layers of abstraction and delegation within your multi-agent system.
    • Interaction: High-level agents primarily use LLM-Driven Delegation or Explicit Invocation (AgentTool). They assign tasks to their subordinate sub-agents. Once sub-agents complete their tasks, their results return up the hierarchy. This occurs either through direct tool responses or by updating the shared session state.

Review/Critique Pattern (Generator-Critic) with ADK Primitives

The Review/Critique Pattern enhances output quality through a dedicated review process.

  • Structure: This pattern typically involves two distinct agents, often contained within a SequentialAgent: a Generator agent that produces an output, and a Critic/Reviewer agent that evaluates or critiques that output.
  • Goal: The objective is to significantly improve the quality, accuracy, or validity of generated output. This happens by systematically having a specialized agent review it after its initial creation. This acts as a quality control mechanism within your multi-agent system.
  • ADK Primitives Used:
    • Workflow: A SequentialAgent is essential here. It strictly ensures that the generation step (by the Generator) completes before the review process (by the Reviewer) can begin. This sequential execution is fundamental.
    • Communication: Shared Session State is critical for communication. The Generator agent uses its output_key to save its generated output to the shared state. Subsequently, the Reviewer agent reads that specific state key to access the content it needs to critique. The Reviewer might then save its feedback or a status (e.g., “valid” or “invalid”) to another state key for any further downstream steps.

Iterative Refinement Pattern with ADK Primitives

The Iterative Refinement Pattern is used for progressively improving results.

  • Structure: This pattern leverages a LoopAgent. This agent contains one or more agents designed to work on a specific task over multiple iterations.
  • Goal: The core objective is to progressively improve a result (which could be code, text, a plan, or any other output) stored in the session state. This iterative process continues until a predefined quality threshold is met, or a maximum number of iterations is reached. This ensures continuous improvement within your multi-agent patterns using ADK primitives.
  • ADK Primitives Used:
    • Workflow: The LoopAgent is the central primitive. It effectively manages the repetition and execution of its sub-agents over multiple cycles.
    • Communication: Shared Session State is absolutely essential for this pattern. Agents within the loop must read the previous iteration’s output (stored in the state). Then, they save their refined version back to the state for the next iteration.
    • Termination: The loop’s execution typically concludes based on a predefined max_iterations count. Alternatively, a dedicated checking agent can set escalate=True within the Event Actions. This occurs when the generated result is satisfactory, effectively breaking out of the loop.

Human-in-the-Loop Pattern with ADK Primitives

The Human-in-the-Loop Pattern integrates essential human oversight into AI workflows.

  • Structure: This pattern weaves points of human intervention directly into an automated agent workflow. This allows for human oversight, approval, correction, or the execution of tasks that AI cannot yet perform effectively.
  • Goal: The primary objective is to allow for critical human oversight, approval, correction, or completion of tasks currently beyond AI capabilities. This ensures accuracy, compliance, or handling sensitive decisions within your multi-agent patterns using ADK primitives.
  • ADK Primitives Used (Conceptual):
    • Interaction: This pattern can be implemented conceptually using a custom Tool. This tool pauses the agent’s execution. It sends a request to an external system (like a user interface or ticketing system), waiting for human input. Once the human provides a response (e.g., “approved” or “rejected”), the tool returns this decision to the agent, allowing the workflow to resume.
    • Workflow: LLM-Driven Delegation (e.g., using transfer_to_agent) can trigger human intervention. This targets a conceptual “Human Agent” that activates the external human review workflow. Alternatively, the custom tool can be directly used within an LlmAgent where human input is needed.
    • State/Callbacks: The shared session state can store task details for human review. Callbacks or polling mechanisms manage the asynchronous interaction flow, awaiting the human’s decision.
    • Note: ADK currently lacks a built-in “Human Agent” type. Implementing this pattern requires custom integration to bridge the ADK agent system with an external human interaction system.

Conclusion:

The advent of multi-agent systems represents a significant leap in AI capabilities. It allows for the decomposition and collaborative solution of increasingly complex problems. By mastering these common multi-agent patterns using ADK primitives, developers can design, build, and deploy sophisticated AI architectures. These architectures are modular, efficient, and highly effective.

The patterns explored here Coordinator/Dispatcher, Sequential Pipeline, Parallel Fan-Out/Gather, Hierarchical Task Decomposition, Review/Critique, Iterative Refinement, and Human-in-the-Loop provide powerful starting points. The true strength of ADK lies in its flexibility. It enables you to mix and match these primitives as needed. This creates the most effective and intelligent multi-agent system for your specific application requirements. As AI continues to evolve, the ability to orchestrate collaborative AI will be a cornerstone of cutting-edge development.

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