Information technology / Insurance data analytics
Verisk: Connecting requirements, implementation, review and testing in insurance software delivery with stage-specific AI agents
- Company
- Verisk
- Country
- Not classified
- Adoption stage
- In operation
- Source published
- Date basis
- The date the source was published. It can differ from the date adoption started.
- How the source was checked
- Read the full source text
The work problem
After adopting coding tools, Verisk engineers still handled requirement clarification, task decomposition, test planning and code review through manual handoffs. The story co-written by AWS and Verisk describes repeated clarification and manual work caused by incomplete acceptance criteria and context lost between stages. Software used in insurance also needs a record of who reviewed and approved a change, not just faster delivery.
Technology and data
Verisk built separate Requirements, Grooming, Developer, Code Review and QA agents on Amazon Bedrock and AgentCore. Inputs include Jira tickets and attachments, repository code, API specifications, internal documents and artifacts from earlier stages. MCP connects existing enterprise tools, and Jira state changes or pull requests trigger individual agents. The Developer Agent uses Claude Code on Bedrock. Product owners approve requirements, developers approve implementation and review decisions, and QA engineers approve test plans. Validated artifacts accumulate as context for the next stage.
Results
AWS and Verisk report that, for features developed through the complete workflow, test planning fell from 3 to 4 hours to under 10 minutes, and automation setup fell from 1 to 2 days to under 30 minutes. More than 90% of agent outputs were accepted by human reviewers with minimal modification. Cost per story point, a measure of work size, was 20% to 30% lower when labor and AWS compute were counted together. Weekly releases are an engineering-team target, not an achieved result.
Limits and open questions
The number of measured features, measurement period and composition of the comparison group were not disclosed. Equivalent effects across all Verisk engineering teams have not been established.
Sources
- Reinventing software delivery with AI-SDLC on Amazon Bedrock AgentCoreAWS for Industries, Accessed
Compiled from public sources. These are not results from ATF Works customers.