Cruvero - AI Agent Ecosystem Platform
A production-grade, Temporal-native AI agent orchestration platform. 90,000+ lines of Go powering durable multi-agent workflows, neuro-inspired intelligence, enterprise governance, …
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7 min read
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Roy Gabriel
DevOps Architect · Applied AI Engineer
From embedded firmware to AI agent orchestration, I ship systems that keep working under pressure.
01.
What I Build
AI Agent Systems
Production LLM systems and multi-agent orchestration in Go. MCP servers that connect AI to real tools and data.
Infrastructure & Platform
Kubernetes, GitOps, and SRE at scale. The foundation production AI runs on.
Architecture & Strategy
Two decades of systems design across multiple domains, building at every layer of the stack.
Featured Work
Go MCP Server Ecosystem
Production-grade MCP servers in Go that expose iCloud, Todoist, and Notion as safe, typed tools for LLM agents.
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4 min read
03.
The Journey
Not a resume, chapters that shaped how I build production systems.
Chapter 1
Embedded Discipline
Embedded systems, microcontrollers, and PLCs: hard constraints and firmware that had to be correct the first time.
Embedded Systems
Chapter 2
Production Software
Security systems where mistakes have consequences: clear boundaries, defensive design, and real production discipline.
Security
Chapter 3
Technical Product Ownership
Bridged engineering and product, turning requirements into designs, and designs into outcomes.
Product
Business Services
Chapter 4
Platform and Observability Architecture
Built platforms teams rely on: reliability, observability, and operability baked in from day one.
Architecture
SRE
Platforms
Chapter 5 - Now
DevOps + SRE + Applied AI
Production AI in Go: durable agent workflows, MCP server ecosystems, and the foundations that keep them running.
AI/LLM
Go
DevOps
MCP
Roy Gabriel
DevOps Architect · Applied AI Engineer
I've spent 20 years building systems across embedded systems, micro-controllers, PLCS, security platforms, fintech, SRE, and platform architecture. Today I focus on production AI systems in Go: multi-agent orchestration, MCP server ecosystems, and the DevOps platforms that keep them running. I care about systems that work under pressure: observable, recoverable, and built to last.
05.
Most AI prototypes ship in Python. I ship production AI systems in Go.
Prototypes are cheap. Production is expensive: latency budgets, deploy footprint, operational load, and failure modes matter.
Go’s concurrency model, performance profile, and single-binary deploy story make it a strong fit for production AI systems that must run continuously and predictably.
My work lives where AI meets production reality: reliable platforms, durable workflows, and tooling that can take real traffic.
06.
Contact
If you have a question or want to connect, send a message here.