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Module 19: Advanced Multi-Agent Architectures

Theory​

Combining Workflows for Complex Systems​

You have learned the fundamental multi-agent patterns: dynamic routing with a Coordinator, fixed pipelines with SequentialAgent, and concurrent execution with ParallelAgent. The true power of multi-agent systems comes from combining and nesting these patterns to orchestrate sophisticated, real-world workflows.

The Parallel Pipelines Pattern​

A common advanced pattern is to run multiple, independent, multi-step processes at the same time. This is ideal for complex data gathering and processing tasks.

  • Structure: A ParallelAgent acts as a container for multiple SequentialAgent instances.
  • Use Case: A content publishing system that needs to generate three different types of articles (e.g., a news summary, a social media trend report, and an expert opinion piece). Each article type has its own sequential pipeline (e.g., Research -> Draft -> Edit), and all three pipelines can run concurrently to save time.
       ┌─ Sequential Pipeline A (e.g., News Research) ───┐
       │      (Fetch → Summarize)                        │
       │                                                 │
User ──┼─ Sequential Pipeline B (e.g., Social Analysis) ─┼─→ Multiple, Independent Results
       │      (Monitor → Analyze)                        │
       │                                                 │
       └─ Sequential Pipeline C (e.g., Expert Opinion) ──┘
             (Find → Extract)

              ParallelAgent Container

This architecture is highly efficient. The total time for the parallel phase is determined by the single longest-running sequential pipeline, not the sum of all of them.

In the lab, you will build a system that uses this pattern for a complex research phase, and then combines it with the "Fan-Out/Gather" pattern by adding a final sequential phase to synthesize all the results into a single, coherent output.

Key Takeaways​

  • Advanced multi-agent systems are built by combining and nesting fundamental patterns like Coordinator, SequentialAgent, and ParallelAgent.
  • The "Parallel Pipelines" pattern involves running multiple SequentialAgent instances concurrently within a ParallelAgent.
  • This architecture is highly efficient for complex data gathering and processing, as the total time is determined by the longest-running pipeline, not the sum of all of them.
  • You can combine this pattern with a final "gather" or "synthesis" step to create a complete, end-to-end workflow.
  • Human-in-the-Loop Consideration: For scenarios requiring human intervention (e.g., approval of research findings), the logic would typically be implemented within a Custom Function Tool (e.g., approve_research_tool). If the human does not approve, this tool could either use tool_context.actions.transfer_to_human() (if the UI supports human hand-off) or simply return a status: error to the agent, effectively pausing or altering the workflow until the issue is resolved. This integrates the concept of human oversight with the ToolContext mechanism.