Playbooks, implementation guides, and operating notes for teams building durable AI capability across strategy, data, platform, automation, and governance.
Why production inference bills always exceed estimates — and the Finance-Engineering governance framework for per-agent budgets, model routing, and cost forecasting without capability degradation.
Most shadow AI articles end with a vendor pitch for DLP software. This one ends with a pipeline that turns unsanctioned tools into sanctioned ones — and tells you how to run the discovery program first.
When agentic tools push engineering velocity past 2–3x, product definition becomes the binding constraint. A three-tier decision rights model for VP Eng and product leaders restructuring PM authority without creating chaos.
An honest 5-stage AI maturity assessment scored across 5 independent dimensions. Includes anti-stages, regression patterns, and a 30-minute self-assessment rubric for CTOs and engineering leads.
Agent quality degrades without errors. Learn to detect silent degradation using output fingerprinting, semantic drift detection, and user behavioral signals.
Most teams treat evals as an afterthought. Here's how to build the evaluation infrastructure that catches failures before your users do — from CI gates to production monitoring.
80% of agentic AI projects stall on data gaps. A 10-question self-assessment to score your true readiness for autonomous agent decisions.
A practitioner's guide to building enterprise memory infrastructure for AI agents — covering embedding strategies, chunking decisions, vector stores vs knowledge graphs, and context window management that actually works at scale.
Most AI use case selection happens in workshops where the loudest opinion wins. Process mining uses actual event logs to surface which workflows are genuinely worth automating — and whether they need an LLM or just an RPA bot.
Engineering managers spend hours preparing for 1:1 meetings. An auto-generated intelligence brief pulls Jira metrics, GitHub PRs, and sprint history into a delta-aware summary 30 minutes before each meeting.
Strategy, data, platform, automation, and governance — the full stack of AI transformation.
Twice a week, the strategies, failures, and operating notes from teams actually shipping AI — not theorizing about it.
Viktor Bezdek — VP Engineering, Groupon
Building and writing about AI-native engineering from inside a real organization. These notes are what I wish I'd had when I started.