Agentic AI at organizational scale.
The operating model for coordinating AI-driven engineering. Ten modules. Decision frameworks, not buzzwords. Start anywhere.
CTO
Start with the systemic view: why the org isn't improving, where to invest, and what infrastructure you need.
VP OF ENGINEERING
Start with the delivery view: what's breaking in your review pipeline, how to govern at scale, what to measure.
PLATFORM ENGINEERING
Start with the build view: how specs anchor agent work, what the harness needs, and the architecture decision.
All modules
Read in order or start anywhere.
The Coordination Bottleneck
Why is AI making my engineers faster but my org isn't shipping better?
Outcome: Diagnose why individual speed doesn't become org delivery
Where Your Org Stands Today
What stage are we at, and what should we do next?
Outcome: Score your org across 5 dimensions, get a stage-appropriate plan
Spec-Driven Development
How do I ensure AI builds what we actually need?
Outcome: Why intent definition is the highest-leverage investment, not model selection
Agentic Engineering
How do I coordinate multiple agents and teams without killing the gains?
Outcome: Operating model canvas: team topology shift, agent fleet design, decision tiers
Harness Engineering
How do I trust agent output without reviewing everything manually?
Outcome: Two-layer harness architecture, eval gates, governance RACI
Loop Engineering
How do I move from individual prompting to automated, self-verifying workflows?
Outcome: Loop design template, failure-mode catalog, cost framework
Compound Engineering
How do I make each AI-built feature make the next one easier?
Outcome: Codify checklist and org memory taxonomy
The AI Engineering Control Plane
Build, buy, or hybrid? What does the coordination infrastructure need to look like?
Outcome: Build-vs-buy decision framework, reference architecture, Stripe audit checklist
Metrics That Don't Lie
How do I measure whether this is actually working at the org level?
Outcome: AI-attributed metrics framework, 4-metric CTO dashboard
The 90-Day Plan
What do I do Monday morning?
Outcome: Stage-appropriate 30/60/90 plan across three org sizes
How these connect
Three disciplines, one operating model
Agentic Engineering
How you structure agent work, define decision tiers, and redesign team topology for AI-speed execution. Modules 00, 01, 03.
Deep diveHarness Engineering
How you verify agent output, enforce policy, and build the eval and governance infrastructure that makes autonomy safe. Modules 02, 04, 07.
Deep diveLoop Engineering
How you move from individual prompting to orchestrated, self-verifying workflows, and make the whole system compound. Modules 05, 06, 08, 09.
Deep diveWhy this playbook exists
Engineering leaders are getting the same question from their boards right now: "We've spent 18 months adopting AI tools. Where's the ROI?" Individual productivity is up. Organizational delivery metrics are flat. There's a gap between those two things, and nobody has given you a clear framework for closing it.
This playbook doesn't sell you on a specific AI tool or vendor. It gives you the operating model: the coordination layer, the governance infrastructure, the measurement framework, and the action plan that converts individual AI velocity into organizational delivery.
The terminology will change again. "Agentic engineering" might not be what we call this in two years. The operating model won't. These problems (coordination overhead, governance gaps, verification debt, attribution failures) are structural. They don't go away when the tool names change.
Each module produces one artifact a leader can use immediately: a diagnostic, a self-assessment, a decision framework, an architecture template, a 90-day plan. No buying anything required.
Co-Build Program
From playbook to production
We work directly with engineering leaders who are making this transition now. You bring the real constraints and team context; we help you build the coordination layer around them.