Stateful Claims Workflow Governance Engine with LangChain and LangGraph
Deploying agentic AI into insurance workflows requires continuous human-in-the-loop governance and state auditing. You will construct a stateful claims processing workflow using LangGraph and LangChain that automatically routes high-value claims to human supervisors while processing straightforward losses autonomously. The system must achieve 100% policy governance compliance.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful, auditable claims orchestration graph using LangChain and LangGraph with built-in human-in-the-loop governance checkpoints.
How work is evaluated
Evaluate LangGraph claims workflow compliance across 100 mixed claims cases.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
governance_rule_check
Claims > $50,000 MUST be routed to HUMAN_REVIEW_REQUIRED.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
compliance_rate
Percentage of claims strictly adhering to governance routing logic. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design a state graph schema using `langgraph.graph.StateGraph` representing claim processing stages.
Implement conditional edge routing based on claim threshold rules and risk policy confidence.
Integrate human-in-the-loop interrupt nodes (`interrupt_before`) for manual supervisory approval.
Persist execution states in SQLite / Postgres checkpointers for audit compliance reporting.
Reference links and supporting material
Dataset of 200 property loss scenarios with vary loss amounts, active/cancelled policy states, and fraud markers.
Challenge setup
Evaluate LangGraph claims workflow compliance across 100 mixed claims cases.
Challenge input
JSON containing claim_id, estimated_loss_usd, complexity_rating, policy_status
LangChain
Provides standard prompt templates and tool abstraction layers.
LangGraph
Enables creation of state graphs with human-in-the-loop breakpoints and state checkpoints.
Evaluated output
JSON containing final_status ('AUTO_APPROVED', 'HUMAN_REVIEW_REQUIRED', 'REJECTED'), traversal_path (ARRAY), checkpointer_id
- Claims > $50,000 MUST be routed to HUMAN_REVIEW_REQUIRED.
- Percentage of claims strictly adhering to governance routing logic. • target: 1 • range: 0-1
- Benchmark: LangGraphGovBench-2026
- Compliance Rate target: 100%
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- LangChain · Required
- Langchain · Optional
- Aim · Optional
- LangChain · Required
- Langchain · Optional
Evaluation contract
- governance_rule_check · Weight 1
- compliance_rate · Weight 1
Metrics
- compliance_rate · Target 1
Benchmarks
- LangGraphGovBench-2026
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing claim_id, estimated_loss_usd, complexity_rating, policy_status
Output: JSON containing final_status ('AUTO_APPROVED', 'HUMAN_REVIEW_REQUIRED', 'REJECTED'), traversal_path (ARRAY), checkpointer_id
Execution harness: Python
Evaluation tools
- LangChain · LLM Application Framework
- LangGraph · Stateful Orchestration Engine
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