The Multi-Agent Pipeline Generation Framework

In our last lesson, we saw how the requirement-gathering agent produces a detailed JSON object, the “work ticket,” from its conversation with us. This ticket is now ready to be sent to the “factory,” that is, the back-end multi-agent system responsible for actually building the pipeline.

But how does a team of agents coordinate to build something complex from a set of instructions? ChainBuddy’s backend uses a specific strategy for this: the plan-and-execute orchestration pattern.

This is a direct, real-world application of the pattern introduced in Chapter 1. To understand it, let’s use an analogy: building a house. A construction project doesn’t begin with workers randomly laying bricks.

1. The plan: First, an architect creates a detailed blueprint. This plan lays out every room, every connection, and every component before any construction begins.

2. The execution: Only after the plan is complete do the specialized crews, plumbers, electricians, and carpenters arrive to execute their specific parts of the blueprint.

ChainBuddy’s pipeline generation works in exactly the same way. It is inspired by “plan-and-solve” prompting, a specific technique where a detailed plan is generated before execution. While it functions like the general plan-and-execute pattern, this approach emphasizes creating a complete, step-by-step solution plan that task-specific agents can then follow without deviation.

The entire back-end is built using LangGraph, a library specifically chosen for its ability to create stateful, multi-agent applications that can handle cycles and ensure persistence. This is essential for building complex and adaptive agentic systems.

In the next section, we’ll meet the “architect” of this system: the planner agent.

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