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LangGraph Dialog Orchestration

Description

Uses LangGraph to define and execute stateful, multi-step AI conversation flows as directed graphs. Each node in the graph represents a dialog step — intent detection, slot filling, API call, or human escalation — enabling complex branching conversations that maintain context across turns.

Canonical use case

A bank deploys a LangGraph-orchestrated bot to handle account balance enquiries and fund transfers, with conditional branches for authentication challenges and fraud checks before executing any transaction.

Open Items

  • [ ] Canon alignment — populate canon_axiom_refs or confirm no existing axiom applies
  • [ ] Dependency assessment — set dependencies_assessed: true once SA has reviewed the full chain
  • [ ] effort_estimate — replace 0 with rough engineering days (order of magnitude)
  • [ ] public_description — write the public-facing description before publishing