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Challenge

LangChain Governance Guard: Auditable Agentic Insurance Workflows

Autonomous insurance claims agents require strict operational governance, full auditability, and guardrails to meet regulatory compliance standards. Build a LangGraph stateful workflow using LangChain to enforce policy constraints and pass 100% of synthetic compliance audit checks.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a stateful claims processing workflow using LangChain and LangGraph with step-by-step audit tracing and human-in-the-loop governance controls.

Delivery guide

How work is evaluated

Evaluation

Evaluates compliance rate and audit trail completeness across stateful agent steps.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

compliance_pass_rate

Requires 100% compliance pass rate on threshold policy violations.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

audit_completeness

Percentage of workflow state transitions recorded in audit trace. • target: 1 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Construct a stateful claim processing graph using LangGraph.

  • Implement step-level policy evaluation guardrails using LangChain runnables.

  • Implement automatic escalation gates for claims exceeding financial thresholds.

  • Generate immutable compliance audit traces passing 100% of test scenarios.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 80 claim scenarios testing policy threshold enforcement and compliance guardrail nodes.

How this agent runs

Evaluates compliance rate and audit trail completeness across stateful agent steps.

Challenge input

JSON containing claim_amount_usd, policy_limits, loss_description

LangChain

Standard components for LLM prompt and chain management.

LangGraph

Provides cyclic graph execution and state persistence for governance.

Evaluated output

JSON containing approved (boolean), escalation_triggered (boolean), audit_trail_steps (array)

Checks for
  • Requires 100% compliance pass rate on threshold policy violations.
  • Percentage of workflow state transitions recorded in audit trace. • target: 1 • range: 0-1
Proof of success
  • Benchmark: Agentic Compliance Evaluation Benchmark
  • Audit Completeness target: 1
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
  • Aim · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • compliance_pass_rate · Weight 1
  • audit_completeness · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about LangChain Governance Guard: Auditable Agentic Insurance Workflows