When AI wrote significant portions of the codebase, traditional onboarding breaks — new engineers face code that's locally coherent but globally opaque, without the commit history and PR conversations that carry intent.

Velocity and DORA metrics don't capture what changes when a team adopts AI coding tools — here's what to measure instead, and what the real productivity story looks like at 6 and 12 months.

AI governance that works looks nothing like compliance checkbox governance — it's technical controls, lightweight processes, and clear ownership, not risk committees and policy documents nobody reads.

The EU AI Act's general provisions have applied since August 2026 — here's what the law actually requires of engineering teams building AI-powered products, separated from compliance theatre.

Prompts are production code — they change system behavior, they break in production, and they need version control, approval workflows, and rollback capability just like software deployments.

A production LLM gateway is the single ingress for all LLM calls in your organization — handling routing, fallback, semantic caching, spend limits, and audit logging without teams managing provider credentials directly.

An internal AI platform is the shared infrastructure AI-augmented product teams build on — here's what components it needs, what to build vs buy, and how to avoid the platform team trap.

Multi-agent systems are harder to test than single-agent systems — non-determinism, agent interdependencies, and emergent behavior require testing strategies that go beyond unit tests and assertion-based checks.

Without a registry, multi-agent systems become a discovery problem — engineers build agents nobody knows about, orchestrators hardcode specialist URLs, and governance is impossible; a registry fixes all three.

Multi-agent systems fail in more ways than single-agent systems — specialist timeouts, partial results, cascading failures, and context overflow each need different handling patterns.