# What would an underwriter need before trusting an agent's risk report?

TerraRisk Agent: Use-case design, Python. Repository created Oct 2025.
Code: https://github.com/jlov7/TerraRisk-Agent
Page: https://jasonlovell.ai/projects/terrisk-agent

A reference underwriting copilot for geospatial risk, with signed and traceable reports.

## Design choice

Every decision the agent makes writes an action credential, a deny-by-default policy bundle sets what it may do and spend, and each report carries checksummed artefacts ready for keyless signing.

## In practice

When an agent's analysis will inform a regulated decision, design the audit trail and the policy limits with the workflow, not after it.

## What I built

- Natural-language questions decomposed into geospatial analysis steps
- FEMA National Risk Index and BigQuery Earth Engine connectors
- Action credentials for every decision, and OPA/Rego policies that deny by default, cap spend and keep PII at county level
- Reports as PDF, GeoJSON and CSV with checksums and OpenTelemetry traces

## Where it stops

A personal R&D reference implementation, not a product. It runs offline on synthetic fixtures by default; Google Earth AI stays stubbed until API access is available, and cloud mode needs your own BigQuery and Earth Engine access. Its evaluation harness (golden questions, ROUGE-L, join integrity) checks the reports' narrative and data joins, not underwriting accuracy.

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By Jason Lovell. Independent work, separate from my role at PwC.