Multi-Agent Systems

Multi-Agent Systems (MAS) in ADK TypeScript enable you to build sophisticated applications by composing multiple specialized agents. Instead of creating one monolithic agent, you can structure your application as a hierarchy of collaborating agents.

Why Multi-Agent Systems?

As agentic applications grow in complexity, multi-agent architectures provide significant advantages:

• Modularity: Break complex problems into manageable, specialized components
• Reusability: Use specialized agents across different workflows and applications
• Maintainability: Easier to update and debug individual agent components
• Scalability: Distribute workload across multiple agents and systems
• Specialization: Create agents with focused expertise and capabilities

Agent Composition Types

You can compose various types of agents to build multi-agent systems:

Core Primitives

Agent Hierarchy

The foundation of multi-agent systems is the parent-child relationship:

• Tree Structure: Agents form hierarchical relationships through sub_agents
• Single Parent: Each agent can only have one parent agent
• Navigation: Use parent_agent and find_agent() to traverse the hierarchy
• Scope Definition: Hierarchy defines delegation scope and workflow boundaries

Workflow Orchestration

Specialized orchestration agents manage how sub-agents execute:

• Sequential: Execute agents one after another in order
• Parallel: Run multiple agents simultaneously for faster processing
• Loop: Repeat agent execution until conditions are met

Communication Mechanisms

Agents coordinate through multiple communication patterns:

Communication Patterns

Shared Session State

The most fundamental communication mechanism:

• Passive Communication: Agents read and write to shared session state
• Data Flow: Use output_key to automatically save agent results
• State Management: Track changes through callback mechanisms
• Pipeline Coordination: Perfect for sequential workflows and data processing

LLM-Driven Delegation

Dynamic agent routing based on natural language understanding:

• Intelligent Routing: LLM agents choose appropriate sub-agents for tasks
• Transfer Function: Agents use transfer_to_agent() to delegate work
• Context Switching: Framework handles execution context changes
• Flexible Coordination: Adapts to varying inputs and requirements

Explicit Invocation

Treat agents as tools for controlled interaction:

• AgentTool Integration: Wrap agents as callable tools
• Synchronous Calls: Explicit agent invocation within workflow
• Result Capture: Automatic handling of agent responses and state changes
• Controlled Execution: Precise control over agent interaction timing

Design Patterns

Hierarchical Delegation

Create coordinator agents that delegate to specialized sub-agents based on task requirements.

Pipeline Processing

Build sequential workflows where each agent processes and transforms data for the next agent.

Parallel Processing

Use parallel agents to handle independent tasks simultaneously for improved performance.

Iterative Refinement

Implement loop-based workflows for progressive improvement and quality assurance.

Best Practices

Agent Design

• Single Responsibility: Each agent should have a clear, focused purpose
• Clear Interfaces: Design predictable input/output patterns
• Descriptive Names: Use names that clearly indicate agent capabilities
• Minimal Dependencies: Reduce coupling between agents

State Management

• Consistent Keys: Use standard naming conventions for state keys
• Data Validation: Validate state data before processing
• State Cleanup: Clean up temporary state to prevent memory leaks
• Conflict Prevention: Avoid state key conflicts in parallel execution

Error Handling

• Graceful Degradation: Handle sub-agent failures appropriately
• Recovery Strategies: Implement retry and fallback mechanisms
• Error Propagation: Decide which errors to handle vs. propagate
• Monitoring: Track agent health and performance metrics

Performance Considerations

Resource Management

• Concurrent Limits: Control the number of simultaneously executing agents
• Memory Usage: Monitor memory consumption in complex hierarchies
• Network Resources: Manage external API calls and rate limits
• Timeout Handling: Implement appropriate timeouts for agent operations

Optimization Strategies

• Agent Pooling: Reuse agent instances when possible
• Lazy Loading: Load agents only when needed
• Caching: Cache frequently used agent results
• Load Balancing: Distribute work across available resources

Testing Multi-Agent Systems

Unit Testing

• Individual Agents: Test each agent in isolation
• Mock Dependencies: Use mocks for sub-agents and external services
• State Testing: Verify state management logic
• Error Scenarios: Test failure modes and recovery

Integration Testing

• End-to-End Workflows: Test complete multi-agent scenarios
• Communication Testing: Verify agent interaction patterns
• State Flow Testing: Validate data flow between agents
• Performance Testing: Measure system performance under load

Monitoring and Observability

• Agent Tracing: Track execution flow through agent hierarchy
• Performance Metrics: Monitor execution times and resource usage
• Error Tracking: Log and analyze agent failures
• State Monitoring: Track state changes and data flow

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