The Senior Engineer's Playbook for the Agentic Era
The engineers and companies that learn to architect with AI agents — not just use AI tools — will dominate the next decade. I help you make that shift.
23 years building systems at
AI isn't replacing engineers. It's replacing engineers who refuse to become architects.
You've spent years mastering your craft. Now every headline says AI is coming for your job. Cursor, Copilot, Devin, Claude Code — they write code faster than you can type. But here's what nobody is telling you: the engineers who understand how to architect systems around AI agents are becoming the most valuable people in every organization.
The gap isn't between "AI users" and "non-AI users." It's between engineers who use AI as a fancy autocomplete and engineers who design entire systems where AI agents own workflows end-to-end.
Learn to architect with AIYour clients are asking why they need a team of 20 when an AI agent can scaffold an entire microservice in minutes. Your competitors are already restructuring. The firms that survive won't be the ones that "add AI to their pitch deck."
They'll be the ones that fundamentally re-architect how they deliver software — with AI agents embedded into every phase of the SDLC. This isn't about replacing your people. It's about making each engineer 10x more effective through AI-native architecture.
Transform your delivery modelI don't teach prompt engineering. I teach the architectural thinking required to build software in a world where AI agents are first-class participants in your system — not afterthoughts.
Stop thinking about "writing code faster with AI." Start designing systems where AI agents own entire workflows — with proper guardrails, fallback mechanisms, and quality gates that you design.
Agent orchestration, tool-use architectures, human-in-the-loop systems, RAG pipelines, and multi-agent coordination. These are the design patterns of the next decade.
AI agents generate code fast. But who ensures it's correct, secure, and maintainable? You do. Build automated validation pipelines and testing frameworks designed for AI-generated code.
Demo apps are easy. Production is hard. Observability for AI agents, cost management, latency optimization, failure recovery — the operational concerns that separate toys from real systems.
The engineers who thrive will combine deep domain knowledge with AI orchestration skills. Learn how to translate business domains into AI-native architectures that solve real problems — not just generate boilerplate. Whether it's fintech, healthcare, or SaaS, domain expertise becomes your unfair advantage.
From C/C++ in 2000 to Solana ZK proofs today. I didn't just observe paradigm shifts — I led them inside major organizations.
Deep technical knowledge, distilled into practical guides for engineers making the transition.
From Basics to Real-world Projects
A Mental Model Shift for Experienced Developers
Hands-On Modern Systems Programming