Building Production LLM Agents That Actually Ship: Lessons from 10+ Years in the Trenches
Tool-calling agents, RAG pipelines, and the automation stack that turns "AI demo" into "AI in production" — and why I'm opening up for new projects.
The gap nobody talks about
Most AI content online is about getting a model to respond well in a chat window. The harder, less glamorous problem is getting that same model to reliably book an appointment, update a CRM record, or hand off to a human at exactly the right moment — in production, at 2am, when an API times out or a timezone field silently defaults to the wrong value.
That gap — between "cool demo" and "system a business can depend on" — is where I've spent most of my career, and it's the thread that connects everything I build now.
What "shipped" looks like
A few examples from recent and past work:
RAG systems that refuse to guess. I built a health-data RAG pipeline with embeddings, cosine retrieval, inline citations, and — critically — honest refusal when the retrieved context doesn't support an answer, plus a full audit log. In healthcare-adjacent domains, a system that says "I don't know" is more valuable than one that sounds confident and is wrong.
→ health-data-rag.vercel.app · source
Voice + SMS agents wired into real CRM systems. Built and debugged a full lead follow-up and appointment-booking system combining GoHighLevel, Retell AI (voice/chat), and n8n for a multi-location client. The interesting engineering wasn't the AI — it was the plumbing: explicit timezone configuration per calendar (never inherit it), Luxon expressions in n8n's startTime field, correctly distinguishing a status === "booked" response from a missing statusCode field, and drawing a hard architectural line so voice-generation and SMS-sending never leak into each other. Small details like these are the actual difference between a bot that works and one that quietly fails a week after launch.
Automation across the LLM app surface. From FastAPI backends and Next.js frontends to n8n/Make.com workflows and multi-agent orchestration, I own the full chain: agent logic, retrieval, inference APIs, and the interface around them — not just the prompt.
Open source, not just employer work. Contributing to Inglorious Forge — SSX compatibility testing, a merged ElementSize entity, an error boundary with passing unit tests, a before/after slider component. Reading someone else's production codebase closely enough to contribute cleanly is its own skill, and I think it shows in how I approach client handoffs.
The stack I reach for
Python FastAPI TypeScript Next.js Supabase/PostgreSQL (pgvector, RLS) n8n Make.com GoHighLevel OpenAI API Anthropic API Retell AI Vercel
What I'm looking for
I'm currently taking on new freelance projects — particularly:
- AI agent / chatbot systems wired into real business tools (CRMs, calendars, ticketing)
- RAG pipelines that need to be trustworthy, not just impressive in a demo
- n8n / Make.com automation connecting AI to existing workflows
- Integration debugging — if your AI feature "mostly works" and you can't figure out the 10% that doesn't, that's often exactly my kind of problem
If any of that matches something you're building, I'd genuinely like to hear about it.
Luis Cruz
Senior AI Developer — LLM Agents, RAG, Automation
📧 Emails are not allowed
🔗 topstar-ai-github-io.vercel.app · GitHub · Contra