An0n_Jon
Orloj is a runtime with policies you apply as guardrails to keep agents in check for tools and other limits which all happen at the runtime level. And building out the tooling it just made sense to containerize tools so they can be spun up on demand and stay alive when needed and spin down after.
The way every agent framework handles MCP is a latent security problem
When you configure MCP servers in any agent framework today, they all connect at session init and stay connected for the entire session.If your agent is set up with 12 integrations, all 12 are live from the moment the session starts. Holding connections, processes, and attack surfaces. Even if 9 of them never get called.If you're hosting your own MCP servers, you're paying for all of them 24/7...
Show HN: Orloj – agent infrastructure as code (YAML and GitOps)
https://github.com/OrlojHQ/orloj
Hey HN, we're Jon and Kristiane, and we're building Orloj (https://orloj.dev), an open-source (Apache 2.0) orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, and reliability.We built this because running AI agents in production today looks a lot like running...
Instead of only showing symptoms (CrashLoopBackOff, etc), it builds a dependency graph of cluster resources and tries to answer:
"Why did this actually break?"
Example failure chain it can detect:
Secret/prod/db-password missing ↓ Pod/prod/payments-api CrashLoopBackOff ↓ Service/prod/payments has no endpoints ↓ Ingress returning 503
Would love feedback from people running Kubernetes in production.