How do you build a project with agentic AI development?

I drive AI coding agents (Claude Code, Codex, Cursor) with custom rules, hooks and context, spec-first — then gate everything with tests and AI review so speed never costs quality.

How do you build a project with agentic AI development?

I drive AI coding agents (Claude Code, Codex, Cursor) with custom rules, hooks and context, spec-first — then gate everything with tests and AI review so speed never costs quality.

AI consulting & engineering

Building software with agentic AI development

Q: How do you actually implement AI in a business?

I find where AI creates real value, design the architecture, and build it end-to-end — running systems, not slideware.

Q: Can you build AI into a healthcare product?

Yes — I have architected clinical decision support, AI meal analysis and LLM health assistants on a custom model + vector database, with the privacy and reliability healthcare demands.

Q: Can you build a production mobile app in React Native?

Yes — I ship native iOS and Android apps from one Expo/React Native codebase, with payments, provisioning, offline-first flows and clean native integrations.

Q: How do you scale a Node.js backend?

With the right architecture: services split by load, event streaming, queues and caching, observability, and databases chosen per workload — proven on a platform that grew to 3M+ users.

Q: Can you build an MVP and then scale it?

Yes — I build MVPs end-to-end fast, then evolve the same codebase to production scale instead of throwing it away. Several of mine grew to acquisition or millions of users.

Q: How do you use CodeRabbit for code review and quality?

CodeRabbit reviews every pull request automatically — catching bugs, security issues and smells — as a gate alongside human review and a real test suite, so nothing weak reaches main.

Q: Can you build a custom AI model for my product?

Yes — self-hosted models on Ollama (Llama, Mistral, DeepSeek), fine-tuned or RAG-grounded, when you need privacy, control or a task an off-the-shelf API can’t do well.

Q: How do you build a RAG system?

Chunk and embed your data into a vector database, retrieve and rerank the right context per query, ground the model in it, and evaluate — so answers are accurate and cite your sources, not hallucinations.

Q: Which AI model should you use — Claude, GPT, Gemini, Perplexity or Grok?

It depends on the task — and the real answer is usually more than one. I pick and route across frontier models by strength, with evals and fallbacks, so you get the best result without lock-in.

Agentic development is a genuine step-change in how software gets built — but only if it is driven, not trusted blindly. I run AI coding agents as fast, tireless pair-programmers inside a workflow with real guardrails, so you get the speed without the mess.

Agents driven by rules, hooks and context

The difference between an agent that helps and one that thrashes is the setup. I configure Claude Code with custom rules and hooks, pull live library docs through Context7/MCP, and give agents the project conventions up front — so they produce code that matches the codebase instead of generic boilerplate. Codex and Cursor fill the same role where they fit better.

Spec-first, small steps, always reviewable

I work spec-first: agree the change, let the agent implement it in small, reviewable increments, and keep a human in the loop at every step. That keeps the work legible and lets me catch a wrong turn in seconds rather than after a thousand lines.

Guardrails so speed never costs quality

Every agent output goes through the same gates as any code: type-checking, tests (Jest, Vitest, Playwright) and automated review with CodeRabbit before merge. The result is the throughput of agentic development with the reliability your product needs — I built an internal Rust CLI and standardised tooling across projects exactly this way.

Key points

Agents configured with custom rules, hooks and live context (Context7/MCP).
Spec-first, small increments, human-in-the-loop at every step.
Type-checks, tests and CodeRabbit review gate every change.

Tools I reach for

Claude AIOpenAITypeScriptRustJestVitestPlaywright

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Building software with agentic AI development

Agents driven by rules, hooks and context

Spec-first, small steps, always reviewable

Guardrails so speed never costs quality

Related work

Have something like this in mind?

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