These are the principles I actually apply when building products like ARI, enterprise SaaS at Ensamble, and the governance workflow behind Kairo Runtime.
SOLID
- Single responsibility — UI components do not own booking side effects; services do.
- Open/closed — new LLM tools extend the orchestration registry without rewriting the WhatsApp webhook path.
- Dependency inversion — dashboards depend on API contracts, not provider SDKs.
Clean Architecture
Keep dependencies pointing inward: domain rules (booking, qualification) stay free of framework details. Next.js, Express, Groq/Gemini, and WhatsApp adapters sit at the edges. That separation is what made expanding ARI into MAYA feasible without rewriting the core.
Domain-driven thinking
Name things after the business: Conversation, Lead, Booking, Content Job — not after tables or LLM responses. When the domain language is clear, prompts, APIs, and UI labels stay aligned.
API-first
Contracts before screens. Shared error shapes, auth boundaries, and webhook verification come before polish. At Ensamble this reduced inconsistency across internal platforms; at Kairo it keeps the dashboard and automations honest.
Performance
Optimize from measurement: rendering cost, bundle size, Core Web Vitals. On Ensamble platforms, modular architecture, lazy loading, and memoization contributed to ~30% performance gains documented in my CV. No premature micro-optimizations for vanity scores.
Accessibility
Keyboard paths, focus states, semantic structure, and contrast are part of shipping — not a late audit. Contract work at Lumston reinforced accessibility and responsiveness as delivery quality, not optional extras.
Testing
Behavior changes need failing tests first when a suite exists. Until a suite is fully wired, smoke scripts, typechecks, and production observability (Sentry) still gate releases. For agentic work, Kairo Runtime encodes SDD/TDD as governance so "we will test later" is not the default.
Observability
Sentry in production, structured logs around webhooks and booking writes, and an operator dashboard for human-visible failures. AI systems fail quietly if you only watch the happy path.
CI/CD
GitHub Actions, Docker where needed, Vercel/Railway deploys. Releases for @kal-elsam/kairo-runtime use automated publish with npm provenance — the same "ship with evidence" mindset I want in product work.
AI Engineering
- Daily AI-assisted development (Claude Code, GPT, Gemini, Groq) for exploration, implementation, tests, docs, and reviews.
- Full ownership of production decisions — tools accelerate, they do not replace judgment.
- Function calling and prompt engineering live behind service boundaries.
- Prefer systems (orchestration, state, observability) over one-off chat demos.
See also: System Design · How I Built ARI