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Linked challenge: Wildfire Property Risk Underwriting Multi-Agent System with AutoGen & Helicone

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Linked challenge

Wildfire Property Risk Underwriting Multi-Agent System with AutoGen & Helicone

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Set up a multi-agent system using Microsoft AutoGen (`pyautogen`). Create three agents: `GISAnalystAgent` (analyzes spatial and canopy data), `ComplianceAgent` (evaluates structural hardening and defensible space rules), and `UnderwriterAgent` (aggregates findings into an underwriting decision). Configure all agents to route OpenAI API calls through Helicone by setting `base_url='https://oai.helicone.ai/v1'` and passing custom Helicone headers (`Helicone-Auth`, `Helicone-Property-SessionId`).

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Linked challenge

Wildfire Property Risk Underwriting Multi-Agent System with AutoGen & Helicone

With property insurance exposure hitting $1.4T across top wildfire-prone states, insurance carriers and CRE asset managers need automated, multi-modal risk assessment systems that combine geospatial satellite telemetry, local vegetation density, fuel models, and policy compliance rules. Manual underwriting processes fail to scale against escalating climate events and rapidly shifting regulatory standards. In this challenge, you will build an automated wildfire property risk underwriting workflow using Microsoft AutoGen and Helicone for observability. You will construct a multi-agent network comprising a GIS Geospatial Analyst Agent, a Structural Hardening Compliance Agent, and an Actuarial Risk Scoring Agent. The system ingests building coordinates, satellite canopy rasters, structural attributes, and local zoning codes to dynamically output an underwriteability score, hazard mitigation recommendations, and adjusted premium pricing tier. Your deployment must track all agent interactions, tool calls, LLM latency, and cost telemetry via Helicone's observability SDK, enforcing structured JSON output for downstream claims systems.

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