AI-Native 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.

The Vision

• Sinapt: Two Products, Not One — The refined consolidation manifesto. The infrastructure-first split: knowledge-base layer ships first, cockpit comes later as the opinionated UI on top.
• Sinapt and the Queryable Company — The market thesis. Companies that win the AI era will have their entire institutional knowledge queryable by agents as one layer; the rest will keep paying the knowledge tax. Why incumbent wikis-with-AI-bolted-on can't get you there.
• The AI-Native Software Engineer — What this engineer actually looks like in 2026. The role that emerges when orchestration replaces typing as the core skill.
• The Last Interface — Where developer tooling is going as AI absorbs more of the surface. The trajectory toward zero-friction operator experience.
• Decade Zero — The timeline this all sits within. Why the next ten years are the consequential ones for how software gets built.
• SaaSpocalypse — The user-developer collapse, made visceral. Wall Street saw $285 billion vanish in 24 hours; the version on operators' desks has been running quieter and ahead of the public-market thesis by months. What dies, who wins, and why personal SaaS falls before enterprise CRM.
• Sinapt: The Architecture Before the Code — The build update behind the manifestos. What I locked before writing PoC code: Floor 4 retrieval (hybrid + reranker + contextual retrieval), permission-aware retrieval (ACL filter applied before the engine sees data), four contract surfaces sharing one authorization spine (MCP, REST, CLI, Web UI), the ingest loop, and the open multi-writer merge problem. The architecture report that shows how the agent-first knowledge substrate is actually built, not just argued for.
• Claude for Legal Is Claude Code Wearing a Suit — The vertical thesis with hard architecture evidence. Anthropic's May 12 launch verticalizes the same plugin/MCP/agent/managed-runtime stack that powered Claude Code into a legal SKU. The five-layer template (plugin + MCP + surface + managed agent + grounding) is the production-ready pattern for vertical AI on a general-purpose foundation model in 2026.
• Grok Build — The convergence proof point. xAI shipped a coding agent CLI with full Claude Code convention compatibility: AGENTS.md, MCP, Skills, plugins, hooks. The agent-native operating layer is now a portable industry standard. Bet on the layer, not the terminal app inhabiting it this quarter.

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.
• KISS Your AI Workflow — The discipline of subtracting. Every component you keep has to earn its place against the cognitive load it adds.
• MCP, From Pidgin to Protocol — The complete reference for Model Context Protocol: what it is, when to use it instead of REST or vendor function calling, every server worth running, and the security failure modes most teams hit. Five floors from ELI5 to the cutting edge.
• Codex Mobile — Route tasks by execution boundary. The decision framework for choosing between Claude Code Remote (local truth), Codex cloud (scoped delegation), and Codex mobile (connected host supervision). The phone is the interrupt layer. The laptop, devbox, or cloud is the execution layer. The human is the policy layer.

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.
• Two Models, One Branch — The refined multi-agent code review pattern. Different model, different blind spots, second opinion as muscle memory.
• Above the Model — The components above the model that decide AI-native output quality. Context engineering, Claude Dreaming, sycophancy mitigation, instructions, tool curation, evals, and verification — plus a 90-day operator plan. The flagship operator's manual.
• My Agent Filed Its Own Ticket — The prompt-injection lite story plus the Lethal Trifecta + Rule of Two frameworks + actionable operator defenses. Why model-layer fixes are not enough and what to do about it.

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.
• The Exomind — What happens when you apply the cockpit pattern to your entire life, not just code. The KB-as-second-brain extension.
• RAG, From Crayons to PhD — The complete reference for retrieval-augmented generation: what it is, when to use it, when to run, every tool worth caring about, and the production failure modes most teams discover the hard way. Five floors from ELI5 to PhD.

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.

vanja@petreski.co · LinkedIn · X · petreski.co