Multi-Agent Systems with ADK-Rust: Orchestration and Delegation

Explore how to build complex multi-agent systems in Rust using ADK, from simple delegation to sophisticated graph-based orchestration.

Posted on: 2026-03-23 by AI Assistant

Building intelligent systems often requires more than a single agent. As tasks grow in complexity, the need for specialized agents working together becomes critical. ADK-Rust provides robust features for building multi-agent systems, allowing you to compose teams of specialists to handle intricate workflows.

In this post, we’ll explore the four primary ways to orchestrate multiple agents using ADK-Rust.

1. Sub-Agents and Delegation

The most fundamental approach is adding sub-agents to a parent LlmAgent. When an agent has sub-agents, it gains a built-in transfer_to_agent tool, allowing it to delegate tasks dynamically.

How it works:

• Parent Agent (Coordinator): Receives the initial query and supervises the process.
• Sub-Agents (Specialists): Agents with narrow, specific instructions (e.g., Billing, Support, Data Analysis).
• Delegation: The coordinator uses the transfer_to_agent tool to route parts of the conversation to the most suitable specialist.

Example:

let billing_agent = LlmAgentBuilder::new("billing_agent")
    .instruction("Handle all questions related to invoices and payments.")
    .model(model.clone())
    .build()?;

let support_agent = LlmAgentBuilder::new("support_agent")
    .instruction("Help users with technical troubleshooting.")
    .model(model.clone())
    .build()?;

let customer_service_agent = LlmAgentBuilder::new("customer_service_agent")
    .instruction("Delegate to billing_agent or support_agent as needed.")
    .model(model)
    .sub_agent(Arc::new(billing_agent))
    .sub_agent(Arc::new(support_agent))
    .build()?;

2. Workflow Agents (Deterministic Patterns)

For more structured workflows, ADK-Rust offers dedicated workflow agents that follow deterministic execution patterns.

SequentialAgent (Pipeline)

Runs sub-agents one after another, accumulating the conversation history. Perfect for multi-step processes like Research → Analysis → Summary.

ParallelAgent (Concurrent Execution)

Runs all sub-agents simultaneously on the same input. Useful for gathering information from multiple sources at once (e.g., Translator + Summarizer).

LoopAgent (Iterative Reasoning)

Runs a sub-agent repeatedly until a specific condition—often signaled by an ExitLoopTool—is met.

3. GraphAgent (Stateful Orchestration)

For highly complex systems, the adk-graph crate provides LangGraph-style orchestration. This allows you to define workflows as directed graphs with nodes and edges.

Key features include:

• AgentNodes: Wrap agents as graph nodes with custom data mapping.
• Cyclic Workflows: Native support for loops and iterative reasoning (ReAct patterns).
• State Management: Persistent state for fault tolerance and human-in-the-loop interactions.
• Conditional Edges: Logic-based transitions between nodes based on the current state.

4. Hierarchical Multi-Agent Systems

The real power of ADK-Rust comes from its composability. You can combine these patterns to create deep hierarchies. For example, a SequentialAgent pipeline could include a ParallelAgent as one of its steps, which itself manages a team of specialized LlmAgents.

Conclusion

Whether you need simple delegation or sophisticated graph-based orchestration, ADK-Rust provides the building blocks to design and implement robust, intelligent multi-agent teams.