AI Writes the Code.
You Still Make the Call.

An anonymized teardown of a consumer platform I scaled to several million users. The architecture that carried ~30K req/s at peak, the four walls we hit on the way up — database connections, a cache stampede that caused a 19-minute outage, payment double-charges, and a credential-stuffing attack that looked like organic growth — and the trade-offs behind each fix. Topology, layered caching, the data tier, WAF and rate-limiting stack, and four real ADRs. No vendor named; the engineering is exactly as it happened.

AI moved the engineering bottleneck from writing code to reviewing it — and most org charts haven't caught up. The Generation–Review Ratio, why cutting junior hiring is a five-year trap, the four roles every AI-native team needs, and how to rewrite hiring and leveling for 2026.

Most agent SDLC setups use the LLM as the runtime for everything—including deciding which files to read—which is the biggest source of token waste and non-determinism. SpecLoom flips this: write your spec as typed blocks with IDs and dependencies, and a deterministic compiler emits a minimal, hash-stamped bundle for one task. A real engineer bundle compiles to ~370 tokens instead of 20–60k, the same task always produces a byte-identical bundle, and @spec:ID#hash anchors turn spec/code drift into a CI failure. Covers the .loom format, the Deterministic Context Compiler, tiered budget degradation, the drift gate, engine-enforced persona gates, and a 60-second loop to try it.

A practical reference catalog of the eight architectures worth knowing — layered, modular monolith, hexagonal, event-driven, CQRS + event sourcing, microservices, serverless, and the strangler fig. Each with a diagram, the forces that make it the right call, the failure mode that makes it the wrong one, and a link to runnable reference code. Plus a decision flowchart so you pick on fit, not hype.

It's day three as CTO. Production is on fire, your team is demoralized, and the CEO wants a roadmap by Friday. Your job isn't to fix everything—it's to build a clear picture, earn trust, and set a direction. Here's your survival framework.

Senior engineers waste hours typing the same Copilot prompts repeatedly. GitHub Copilot Agents (.agent.md files) let you encode expertise once, reuse forever. Built 4 production agents that coordinate: reduced article writing 12 hours → 90 minutes. Learn Agent Maturity Model, 3-Gate Validation Framework, Agent Design Canvas, and orchestrator patterns. Real .agent.md files, metrics from 6 months production use.