Scaling an autonomous agent from one team to fifty: A serverless-to-container decision framework
QA teams shipping complex products generate test failures around the clock. Every failure needs a bug ticket with steps, screenshots, The post Scaling an autonomous agent from one team to fifty: A serverless-to-container decision framework appeared first on The New Stack .
As test failures pile up relentlessly for QA teams, the manual process of handling each one consumes an entire engineering day. To eliminate this burden, a system was built that observes test results, decides on the issue, and takes action without any human involvement. This autonomous agent files structured bugs, attaches screenshots, calculates repro rates, and detects regressions.
Initially running on Lambda, the system was perfectly suited for one team. However, scaling the solution to serve multiple teams required reevaluating architectural decisions due to shifting workload characteristics. While Lambda was ideal for handling single-tenant workloads with short-lived, stateless events, multi-tenant environments presented different challenges.
Long-running agents necessitated persistent state, cross-tenant analysis required shared memory, and GPU-burst inference added further complexity. The decision rubric revealed that container-native execution models better accommodated these needs, particularly with the help of EKS Auto Mode. This solution allows for centralized rate limiting, connection pooling, and namespace-level isolation, making it a natural fit for multi-tenant environments where scalability and cross-team intelligence are paramount.
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