I spent 10 years doing cloud solution architecture. AWS Well-Architected Framework, infrastructure governance, cost optimization at scale; that's the mental model I used to think about systems. Then I started seeing teams build AI agents and it was chaos. Same problems cloud solved 15 years ago; no governance, no cost visibility, no clear boundaries around what the system can do autonomously, but nobody had translated those lessons to this space. So I took what worked in cloud architecture and adapted it for agents. Most teams are using LangChain, LangGraph, Claude SDK, orchestration tools are figured out. But the higher-level architectural thinking isn't. You're picking up pieces from blog posts, Discord threads, people's experience. Nobody's got a coherent framework. This framework puts it in one place: Epistemic gates: Where exactly does AI output become real action? What needs to gate it? Cost: Not just cheaper models. Why agents cost what they cost. Context bloat is where most of the waste lives. Autonomy: How much can this agent decide on its own without breaking things? Context: Understanding what's actually needed vs. what you're paying for anyway. I also built an MCP tool to start applying this: agenticaf.io/tools Looking for maintainers, contributors, people to test the MCP tool and reports, folks willing to review the tools against real agent builds. Feedback wanted. What breaks. What's missing? happy building - Dave

Agentic Architecture Framework

Vendor Agnostic Opensource Architecture Framework

Vendor Agnostic Opensource Architecture Framework