What changed
Rescale created an AI triage agent to automate the classification and routing of nightly end-to-end test failures using the AI-Driven Development Lifecycle (AI-DLC) framework on AWS. The agent runs within Rescale’s existing Argo Workflows CI/CD pipeline on Amazon Elastic Kubernetes Service (Amazon EKS). When a nightly Playwright test suite produces failures, the pipeline triggers the triage agent pod. The agent classifies each failure into a root cause category using Claude Opus 4.8 on Amazon Bedrock, retrieves historical failure patterns from a knowledge base, queries pod logs through Model Context Protocol (MCP), and generates a structured remediation report for engineer review.
What this means in practice
The AI triage agent streamlines the debugging process by automatically classifying test failures and routing them to the appropriate team, reducing the time engineers spend on repetitive tasks. This automation helps ensure that end-to-end tests pass before new changes are deployed to production, which is critical for maintaining system stability. The agent completes its workflow in seconds and outputs directly into the team’s existing reporting pipeline, integrating seamlessly with Rescale’s CI/CD processes. By using AI-DLC, Rescale was able to move from problem definition to staging deployment in five days and to production within weeks.
Limitations and availability
The AI triage agent is currently available as part of Rescale’s platform for their internal use. The solution relies on Amazon Bedrock for classification, Amazon EKS for deployment, and Amazon CloudWatch for log retrieval. The agent is built using the Strands SDK and runs as a step within Rescale’s Argo Workflows pipeline. The AI-DLC framework is an open-source methodology that Rescale is extending across their engineering organization. The supplied announcement does not specify the exact performance metrics or benchmark results of the triage agent.
