A frontier lab watched its own Claude monitor get argued out of catching Claude. The case against same-model AI code review, from the vendor's own receipts.
You vet every npm package, then trust an LLM on vibes. Booz Allen's numbers on AI supply chain risk should change how you think about model selection.
How 15 specialized agents go from ADO story to shipped PR — orchestration, context management, quality gates, and drift detection.
Author agents enforce quality structurally — rating AI output, retrying on partial passes, and escalating when something is fundamentally wrong.
The architecture that cut per-session agent context by 85–89% — without losing a single useful rule.
Not all agent context is the same. The difference between skills, instructions, and scripts determines whether your agent stays focused.
Using the most powerful model for everything isn't a strategy — it's expensive and slow. Here's how to match models to tasks.
A single generalist agent doing everything sounds simpler. It isn't. Here's why specialists produce better output and preserve context at handoffs.
AI isn't replacing QE — it's making the discipline more essential. The judgment behind good testing is exactly what AI can't supply.
Prompt engineering isn't new — it's a repackaging of everything good engineers already know about communicating intent precisely.