When you're choosing an agent framework, popularity is the wrong scorecard. Pick the one that fails loudly in development and gracefully in production - or you'll find out in audit.
This article shows how to build a multi-agent architecture reviewer that produces a structured review artifact: normalized findings with severity, evidence, and recommendations, plus clarifying questions and explicit “needs human judgment” flags.
Python dependency management is not a developer problem. It is a team productivity problem that shows up as slow CI, painful on-boarding, and a different tool in every repository.
This article is for the ones building an LLM product and want to know what it actually costs you - per feature, per user, per request - without paying for Datadog or wiring up OpenTelemetry.
We will walk through a concrete RAG example - a pipeline over a corporate annual report - and build the testing layer that most teams skip entirely. The code is real and runnable. The failures are not hypothetical.
