AI-Native Engineering
I help engineering teams adopt AI-native development workflows. Workflow design, knowledge architecture, team training, embedded engineering.
Within two years, every competent engineer will know how to orchestrate AI agents.
Writing specs. Running feedback loops. Evaluating output. Managing knowledge bases. These skills are becoming table stakes — learnable, teachable, and fast-spreading across the industry.
So what separates teams that ship transformative work from teams that ship generic output?
Domain knowledge. The deep, messy, hard-won understanding of your business, your users, your constraints. The edge cases nobody documented. The institutional memory that lives in people, not wikis.
The winning combination — for a person, a team, or a company — is both: AI orchestration mastery and deep domain expertise, connected through infrastructure that lets the two amplify each other. That's what I help teams build.
What I Do
I help engineering teams adopt AI-native development workflows that fundamentally change how fast they ship — and how deep their output goes. Not slide decks. Not best-practice lectures. Real systems, implemented in your codebase, measured by what reaches production.
- Workflow Design — PRD-driven development, spec-driven agentic workflows, multi-agent code review. The complete system from idea to production-ready code, with humans in the loop where they add the most value.
- Knowledge Architecture — Context engineering at scale. How your team captures, structures, and makes retrievable the domain knowledge that feeds every AI interaction. From single-file context to QMD-powered search to LLM-maintained wikis. The infrastructure that turns AI orchestration into sustained competitive advantage.
- Team Training — Hands-on workshops that take your engineers from "I use Copilot for autocomplete" to building features end-to-end with AI agents. Focused on habits and judgment, not tools.
- Embedded Engineering — I join your team and build alongside your engineers, demonstrating AI-native workflows on your actual codebase and your actual problems. Learn by shipping, not by watching.
The Methodology
Everything I teach, I've written about openly. These aren't theoretical frameworks — they're systems I run in production every day. The articles below form a complete curriculum organized around three pillars.
Core Workflow: Orchestrating Agents
- The Architect's Protocol — How to orchestrate AI agents as a senior engineer, not replace yourself with them.
- Spec-Driven Agentic Development — The workflow where human language becomes the programming language and agents build the implementation.
- PRD System for Claude Code — Production-grade requirements documents that AI agents can execute autonomously.
- From AI Skeptic to AI Architect — The transformation from writing every line by hand to orchestrating agents that ship features.
Quality & Verification
- The Third Pass — Two agents and a human. Three independent reviews. How code ships when the domain is too complex for a single pair of eyes.
- Eval-Driven Development — Measuring what matters. The discipline of rigorous evaluation in AI-assisted workflows.
- The Verification Gap — The distance between "AI produced output" and "the output is correct" is the distance of your domain expertise.
- Things AI Is Surprisingly Bad At — Setting realistic expectations. Knowing where AI breaks is as valuable as knowing where it excels.
Knowledge Management at Scale
- The Knowledge Equation — The core thesis. Why domain knowledge, not orchestration, is the real competitive moat in AI-native engineering.
- The Context Wall — When your knowledge base outgrows a single file. QMD-powered hybrid search for markdown knowledge bases.
- The Knowledge Base That Builds Itself — The LLM Wiki pattern. Let the AI maintain your knowledge base so you never edit ctx.md again.
- The Cockpit — Where to store knowledge across multi-repo and collaborative projects. The private orchestration layer that sits above your code.
About
Twenty years of backend architecture, distributed systems, and cloud infrastructure across fintech, healthcare, and gaming. I've shipped production code in Java, Python, Go, and a dozen other languages. I've designed systems that handle millions of requests per day and systems that serve ten internal users — each requires different discipline.
Now I build with AI — the production version, not the demo version. And I help other teams do the same.
Petreski LLC is a US-based software development and consulting company. Based in Medellín, Colombia, working remotely with teams worldwide.
Let's Talk
If your team is ready to stop treating AI as autocomplete and start treating it as an engineering multiplier — reach out. I take on a small number of engagements at any given time, focused on teams that want to build real capability, not just check an "AI strategy" box.