Build a Heat Risk Underwriting Graph with LangChain, LangGraph, and Fireworks AI
With European heatwaves driving severe commercial property risks and business interruption exposure, property insurers require automated tools to assess chronic heat stress and building vulnerability. In this challenge, you will implement a stateful underwriting agent graph using LangChain and LangGraph, powered by ultra-fast LLM inference via Fireworks AI. Your system will ingest commercial property inspection reports, geographical heat hazard data, and HVAC resilience ratings. Using LangGraph state nodes, the graph will route high-risk assets through detailed structural degradation checks, apply policy rate adjustments, and generate a final explainable underwriting summary. By leveraging Fireworks AI's high-throughput API endpoints, the pipeline must execute fast batch evaluations while maintaining high decision fidelity and precise constraint checking across state transitions.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful commercial property heat exposure underwriting graph using LangChain, LangGraph, and Fireworks AI for fast, explainable rate recommendations.
How work is evaluated
Tests the LangGraph underwriting pipeline for state flow completion, classification accuracy, and premium adjustment factor output within latency bounds.
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.
state_graph_completion
Verifies that the graph reaches the END node without unhandled exceptions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
category_classification_match
Ensures output risk category matches expected tier.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
modifier_error_margin
Absolute error between target rate modifier and agent output. • target: 0 • range: 0-0.1
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 stateful workflow graph using LangGraph and LangChain
Configure Fireworks AI model provider endpoints for high-throughput inference
Process commercial property features and heat exposure index values
Generate calibrated risk tiering and premium rate modifier outputs
Reference links and supporting material
Synthetic commercial property dataset containing building specs, regional heat risk scores, and standard actuarial premium adjustment tables across southern European municipalities.
Challenge setup
Tests the LangGraph underwriting pipeline for state flow completion, classification accuracy, and premium adjustment factor output within latency bounds.
Challenge input
JSON with building metadata, location heat index, and cooling infrastructure specs
LangChain
Provides stateful chain and agent workflow abstractions.
Fireworks AI
Offers low-latency inference for real-time agent execution.
Evaluated output
JSON with risk_category (High|Medium|Low), rate_modifier (float), and rationale (str)
- Verifies that the graph reaches the END node without unhandled exceptions.
- Ensures output risk category matches expected tier.
- Absolute error between target rate modifier and agent output. • target: 0 • range: 0-0.1
- Benchmark: UnderwriteBench-HeatStress
- Modifier Error Margin target: 0
- 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 · Optional
- Alibaba Cloud (Qwen) · Optional
- Fireworks AI · Required
- Fireworks AI · Required
- Langchain · Optional
Evaluation contract
- state_graph_completion · Weight 1
- category_classification_match · Weight 1
- modifier_error_margin · Weight 1
Metrics
- modifier_error_margin · Target 0
Benchmarks
- UnderwriteBench-HeatStress
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with building metadata, location heat index, and cooling infrastructure specs
Output: JSON with risk_category (High|Medium|Low), rate_modifier (float), and rationale (str)
Execution harness: Python
Evaluation tools
- LangChain · framework
- Fireworks AI · llm_inference
Recipe freeze marker
No recipe freeze time is recorded.