UnderwriteAI

UnderwriteAI Enterprise Intelligence Platform. Multi-Agent AI Platform for Small Business Insurance Underwriting

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Inspiration

McKinsey: The Future of AI in the Insurance Industry https://www.mckinsey.com/industries/financial-services/our-insights/the-future-of-ai-in-the-insurance-industry

What it does

UnderwriteAI is an institutional Gemini Enterprise Agent Platform that transforms commercial insurance underwriting from a multi-week, manual bottleneck into a transparent, autonomous, and zero-trust multi-agent workflow. Built natively with both the Google ADK (Agent Development Kit) and the official Google GenAI SDK (google-genai), UnderwriteAI reduces commercial intake-to-quote latency from 7 business days down to 3.2 seconds (a 99.8% speedup) while eliminating underwriting blind spots.

When a broker submits an unstructured ACORD application, loss run, or email, the Google ADK Supervisor coordinates a fleet of specialized institutional agents hooking directly into enterprise infrastructure:

The deployed request path on Google Cloud Run follows a strict multi-agent orchestration lifecycle powered by Google ADK and the Google GenAI SDK:

  1. Ingress Dispatch: POST /api/v1/underwrite receives an unstructured commercial application or ACORD payload.
  2. Zero-Trust Security Gate: The Agent Gateway (backend/services/agent_gateway.py) authenticates caller RBAC permissions, verifies regional data sovereignty (us-central1), and invokes Model Armor (backend/services/model_armor.py) to neutralize prompt injection attacks and redact sensitive PII (SSN, EIN, Card Numbers) before payloads reach any downstream model.
  3. ADK Supervisor Invocation: The request is handed to the ADKSupervisor (backend/adk/runner.py), which coordinates the fleet using isolated ADKRunner sessions.
  4. AI Gap Extraction: The Intake Agent (adk_intake_agent in backend/adk/agents.py) invokes the Google GenAI SDK (google.genai) (backend/config.py) with gemini-3.7-flash to extract missing parameters, generating granular two-tier badges with inline text rationale.
  5. Dynamic Tool Grounding & AI Risk Narrative (MCP): The Risk Profiling Agent (adk_risk_agent) executes Model Context Protocol (MCP) tools bound via @adk_tool (backend/adk/tools.py) to query live Open-Meteo weather extremes, FEMA National Flood GIS layers, and USGS seismic feeds, and invokes the Google GenAI SDK (google.genai) with gemini-3.7-pro to synthesize an actuarial risk evaluation narrative.
  6. Actuarial Pricing Gate & AI Endorsement: The Pricing Engine Agent (adk_pricing_agent) applies deterministic actuarial rate multipliers and statutory bounds ($10,000 policy cap), invoking the Google GenAI SDK (google.genai) with gemini-3.5-flash to generate commercial policy endorsement rationales.
  7. Regulatory Compliance Gate: The Compliance Agent (adk_compliance_agent) runs 10 statutory regulatory checks (NAIC licensing, Fair Lending FCRA/ECOA, AML, and Environmental ENV-001), enforcing fail-closed gate logic.
  8. Executive CUO Synthesis: The Feedback & Learning Agent (adk_feedback_agent) invokes the Google GenAI SDK (google.genai) with gemini-3.5-pro / gemini-3.7-flash to synthesize the top board-level Executive Underwriting Summary.
  9. ADK Session Store Persistence: The final state is committed to the ADK Session Store (backend/adk/session_store.py) with an immutable 90-day cold-storage snapshot, enabling 1-click asynchronous session re-hydration (POST /api/v1/sessions/{id}/hydrate).
  10. Live Diagnostics APIs:
    • GET /health: Returns live status showing "adk_status": {"adk_supervisor": "Active", "adk_session_store": "Active", "adk_tools_registered": 8}.
    • GET /api/v1/adk/status: Returns full Google ADK fleet metadata, registered MCP tools, and OpenAPI schemas.

How we built it

  • Dual Google Agent Frameworks: Built natively on both the Google GenAI SDK (google-genai) for frontier reasoning and the Google ADK (Agent Development Kit) for formal agent primitives, MCP tool bindings, supervisor orchestration, and session stores.
  • Security & Governance: Built an Agent Gateway enforcing Zero-Trust RBAC and regional data residency (us-central1). Integrated Model Armor for Zero-Data Retention (ZDR), automated PII redaction, and prompt injection defense.
  • Discovery & Lifecycle: Implemented an enterprise Agent Registry cataloging 10 institutional agents with real-time status tracking, versioning, and latency metrics.
  • State & Persistence: Designed an ADK Session Store and Enterprise Memory Bank capable of capturing historical snapshots and re-hydrating multi-week asynchronous sessions.
  • External Grounding: Implemented the Model Context Protocol (MCP) to query live weather and hazard feeds from Open-Meteo, FEMA, and USGS APIs.
  • Full-Stack Cloud Delivery: Backend built with FastAPI and Python 3.11; frontend crafted in React with high-contrast UI. Containerized with multi-stage Docker and deployed serverlessly to Google Cloud Run.

Challenges we ran into

  1. Eliminating Context Drift Across Weeks of Asynchronous Operations: Commercial underwriting involves waiting days or weeks for external building inspections and audit reports. We solved this by designing the Enterprise Memory Bank, serializing execution graph states into immutable 90-day cold-storage snapshots that can be re-hydrated on demand.
  2. Preserving Deterministic Actuarial Math Alongside Non-Deterministic LLM Reasoning: Underwriting requires exact mathematical rate tables and compliance rules. We decoupled deterministic scoring (risk matrix, premium calculations, NAIC checks) from generative reasoning (intake gap resolution, risk narratives, executive summaries).
  3. Adversarial Resilience & PII Defense in Insurance Documents: Commercial applications frequently contain sensitive tax IDs and social security numbers. We implemented pre-ingress Model Armor scanners to redact PII and intercept adversarial prompt injection attempts before payloads reach any downstream model.
  4. Ensuring Seamless Fallback & Zero-Token Simulation: We built a dual-mode engine allowing the platform to run 100% of its deterministic capabilities in zero-token simulation mode, while automatically cascading across Gemini 3.7, 3.5 models when API keys are enabled.
  5. Eliminating Context Drift Across Extended Timelines: Commercial underwriting involves waiting weeks for external inspections. We solved this by designing the ADK Session Store, serializing execution graph states into immutable 90-day cold-storage snapshots.

Accomplishments that we're proud of

  • 100% Test Suite Coverage: Passed all 45 automated unit and integration tests verifying document parsing, MCP integrations, Google ADK components, risk algorithms, and security filters.
  • Dual Google Agent Framework Integration: Fully integrated both the official Google GenAI SDK and Google ADK.
  • 1-Click Session Hydration: Demonstrated the ability to retrieve multi-week cold-storage underwriting sessions from memory with zero data loss.
  • High-Performance Cloud Run Deployment: Packaged the entire full-stack platform into a lightweight container deployed serverlessly to Google Cloud Run with sub-second response times.

What we learned

  • How to design modular multi-agent workflows where specialized agents collaborate without compounding errors or hallucination risk.
  • The power of the Model Context Protocol (MCP) and Google ADK tool bindings for seamlessly injecting real-time spatial and environmental data into LLM reasoning loops.
  • How to implement Zero-Trust principles in AI applications by treating agent-to-agent and user-to-agent interactions with strict RBAC policies and automated data masking.
  • Best practices for utilizing Google's official modern google.genai SDK and deploying containerized full-stack AI applications on Google Cloud Run.

What's next for UnderwriteAI

  • Direct ACORD & PDF OCR Vision Pipeline: Integrating Gemini Multimodal Vision to extract data directly from handwritten forms, blueprints, and thermal property inspection images.
  • Vertex AI Vector Search Integration: Connecting the Enterprise Memory Bank to Vertex AI Vector Search to perform portfolio-wide loss pattern recognition.
  • Automated Reinsurance Cession & Syndication: Adding specialized Reinsurance Agents to structure and price treaty and facultative reinsurance cessions.

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