Cruvero — AI Agent Ecosystem Platform
June 1, 2024
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1 min read
Summary
Production multi-agent orchestration platform built in Go. Coordinates LLM-powered agents for complex business workflows with durable execution guarantees.
Problem
Existing AI agent frameworks optimize for prototyping speed, not production reliability. They lack durable execution, proper error recovery, and observability — making them unsuitable for business-critical workflows.
Constraints
- Must handle long-running agent workflows (minutes to hours)
- Zero data loss on agent failures or restarts
- Observable: structured logging, distributed tracing, metrics
- Deploy as a single binary per service
Architecture
Go services orchestrating LLM agents via MCP protocol. Durable workflow execution ensures agent tasks complete even through failures and restarts.
Key decisions
- Go over Python: Single-binary deploys, predictable latency, strong concurrency model for managing multiple agent sessions
- MCP protocol: Standardized tool interface allows agents to connect to any data source without custom integrations
- Durable workflows: Agent tasks survive process restarts — critical for long-running AI operations
Outcome
Platform runs production agent workloads with reliable execution guarantees. Agents coordinate across multiple tools and data sources without custom glue code.
Stack
Go, MCP, LLM APIs, Temporal, PostgreSQL, Docker
Authors
DevOps Architect · Applied AI Engineer
I’ve spent 20 years building systems across embedded firmware, security platforms, fintech, and enterprise 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.