4 · LangGraph Orchestration

LangGraph is where the intelligence lives: it orchestrates steps, manages state and decides what to do next. It models agent workflows as a stateful graph of nodes and edges.

How it works

1. 1Receive user input and understand/classify intent.
2. 2Decide if more information is needed; retrieve context (vector search) if so.
3. 3Generate with the LLM via Bedrock.
4. 4Validate/review against guardrails and rules; optionally add human-in-the-loop.
5. 5Post-process, then return the final response.

Key points

• Built on StateGraph — nodes are tasks, edges are transitions (which can be conditional).
• Supports cycles, branching and parallel execution.
• State is shared data passed between nodes (context, memory).
• Perfect for complex, real-world agents that loop until a condition is met.

Why it matters

LangGraph turns simple LLM calls into reliable, production-grade agents by making branching, loops and state explicit and controllable.

Frequently asked questions

What is a StateGraph?

A graph that defines the nodes (steps) and edges (transitions) of an agent, with shared state passed between them.

Why use a graph instead of a chain?

Graphs handle loops, branches and human approval that simple linear chains can't express cleanly.