Every engineering team knows the ritual, and Rescale was no exception. A nightly test suite fails. An on-call engineer pulls the logs, classifies the root cause, and routes it to the right team, only to repeat the process the next morning. At scale, across distributed services and dozens of repositories, this ritual becomes a full-time job hiding inside an on-call rotation.

Rescale is the digital engineering platform built for the AI era. R&D and engineering teams across aerospace, automotive, energy, life sciences, semiconductor, and manufacturing use the Rescale platform to accelerate innovation, bringing intelligent HPC, advanced modeling and simulation, agentic digital engineering, and AI physics together on a single platform.

Beyond coding, Rescale uses AI across the engineering lifecycle, from planning and design through implementation, testing, review, and operations, while keeping engineers accountable for the result. AI can propose and execute, but people define scope, validate the work, and approve production changes. Engineers can then focus on architecture, product judgment, and the context required to operate complex distributed systems.

In this post, we show how Rescale used the AI-Driven Development Lifecycle (AI-DLC) framework on AWS to build an intelligent triage agent. They went from problem definition to staging deployment in five days, and to production within weeks. The post also covers how Rescale is extending AI-DLC across their engineering organization.

As Rescale’s platform grew, so did the operational overhead of keeping it running. Engineers were spending significant time on a repetitive but critical task: classifying automated end-to-end test failures. Hundreds of test cases run in the suite, and when failures appeared, every alert, bug report, and escalation needed to be classified, prioritized, and routed to the right team.

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