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Manual code reviews are a bottleneck that costs your team millions in lost velocity. Here is how I built a multi-agent AI pipeline that catches race conditions, generates property-based tests, and reduced our MTTR by 42%.

Most LLM features die in production because teams treat testing like a vibe check. Here is how to build a rigorous, automated evaluation pipeline using G-Eval, DeepEval, and custom synthetic data generators.

Set up continuous integration and deployment with GitHub Actions. Examples for testing, building, and deploying.