Design Human-in-the-Loop Agentic Governance with LangChain and LangGraph
Integrating autonomous agents into insurance underwriting requires transparent human-in-the-loop governance to satisfy regulators and limit policy exposure. Build a stateful, auditable workflow using LangGraph and LangChain with explicit human approval checkpoints. Achieve 100% compliance with human approval gates for high-risk coverage decisions.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a stateful LangGraph workflow incorporating dynamic routing, checkpointing, and human interrupt steps for high-value risk underwriting.
How work is evaluated
Evaluates if LangGraph correctly pauses for human authorization when risk exceeds pre-set limits.
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.
interrupt_triggered
High-risk submissions always pause for human authorization.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
governance_compliance_rate
Compliance rate with human authorization guidelines • 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
Implement stateful graph workflows using LangGraph.
Design human-in-the-loop interrupt mechanisms for decision validation.
Configure state persistence with memory checkpointers.
Enforce audit log logging for carrier compliance requirements.
Reference links and supporting material
Dataset of 100 commercial underwriting submissions across low, medium, and extreme hazard profiles.
Challenge setup
Evaluates if LangGraph correctly pauses for human authorization when risk exceeds pre-set limits.
Challenge input
JSON submission object with insured_value_usd and property_type
LangChain
Provides foundations for chains and prompts.
LangGraph
Enables stateful human-in-the-loop agent workflows.
Evaluated output
JSON showing execution_state, human_review_required flag, and current_node
- High-risk submissions always pause for human authorization.
- Compliance rate with human authorization guidelines • target: 1 • range: 0-1
- Benchmark: AgenticGovernanceBench
- Governance 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
- AnythingLLM · Optional
- Langchain · Required
- LangChain · Optional
Evaluation contract
- interrupt_triggered · Weight 1
- governance_compliance_rate · Weight 1
Metrics
- governance_compliance_rate · Target 1
Benchmarks
- AgenticGovernanceBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON submission object with insured_value_usd and property_type
Output: JSON showing execution_state, human_review_required flag, and current_node
Execution harness: Python
Evaluation tools
- LangChain · LLM Orchestration Framework
- LangGraph · State Graph Framework
Recipe freeze marker
No recipe freeze time is recorded.