Chapter 7: Workflow Patterns & LangGraph Stateful Graphs
Anthropic's canonical workflow taxonomy — chaining, routing, parallelization, orchestrator-worker — and LangGraph's stateful-graph runtime that operationalizes them at scale, complete with checkpointing, human-in-the-loop, and resumable execution.
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Chapter Overview
Most production LLM systems are workflows, not agents. Anthropic's Building Effective Agents essay (Dec 2024) catalogued the four patterns nearly all LLM apps reduce to. LangGraph (LangChain Inc., 2024) is the leading runtime for these patterns — a stateful graph engine with first-class support for checkpointing, conditional edges, and human-in-the-loop pauses.
This chapter is split: half is taxonomy (which workflow when), half is the practical runtime (how LangGraph implements them).
This chapter covers:
- Anthropic workflow patterns — the canonical taxonomy
- Prompt chaining — sequential decomposition with explicit gates
- Routing & parallelization — fan-out/fan-in patterns
- Orchestrator-worker — planner emits a fixed-shape plan; workers execute
- LangGraph stateful graphs — typed state, conditional edges, the runtime
- Checkpointing & resuming — durable execution and human-in-the-loop pauses
Chapter Roadmap
Click any topic to jump in
The 4 Patterns
Anthropic's taxonomy: chaining, routing, parallelization, orchestrator-worker.
From the taxonomy to the implementations
Prompt Chaining
Sequential decomposition with programmatic gates between steps.
Routing & Parallel
One classifier picks the path, or many handlers run concurrently.
Orchestrator-Worker
Planner emits a fixed-shape plan; stateless workers execute in parallel.
LangGraph
The runtime: typed state, conditional edges, durable execution.
Checkpoints & HITL
Durable execution and human-in-the-loop pauses for sensitive operations.
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