I build reliable AI systems that ship.

Background

My path to AI engineering wasn't a straight line, and that's where the edge comes from.

I started in graphic design and tattoo work, where precision and intentionality aren't optional. Then I spent two years writing deep technical research on complex systems (protocol architecture, incentive design, market structure) that trained me to read an unfamiliar system fast, find where it breaks, and explain it clearly. When I found LLMs and the Model Context Protocol, I stopped analyzing other people's infrastructure and started building my own.

Mexican, operating globally, fully bilingual: Spanish native, English professional. EST-aligned, async by default.

What I do

I build the unglamorous parts that make AI agents trustworthy in production:

• MCP server design: full primitive set (tools, resources, prompts, elicitations, MCP-UI), SSE + HTTP Streamable transport, multi-tenant architecture.
• Multi-tier agentic orchestration: a lightweight router classifies intent, domain-scoped agents run native tool_use loops with self-correction, and an Expert Registry enforces per-domain tool allowlists with live resource injection.
• Observability & cost control: end-to-end Langfuse tracing on every LLM call, tool invocation, model choice, and token cost, with per-session attribution and failure triage without log digging.
• Human-in-the-loop surfaces: one-click approval flows wired into the agent pipeline for irreversible operations.
• Production discipline: 6,000+ tests, 91%+ coverage, CI/CD, schema validation at every boundary, conventional commits, drift guards. The hygiene that makes solo-built work safe to hand to a team.