When I started building omadia — an open-source (MIT), self-hostable runtime for composing AI agents out of plugins — I assumed the hard part would be the model: prompting, tool-calling, getting reliable output.
It wasn't. The LLM bits were mostly solved problems. The parts that ate my time were isolation, data flow, and UX. Here are four lessons I wish I'd internalised earlier.
The project is open source (MIT) and self-hostable — code and docs here: https://github.com/byte5ai/omadia
1. Memory isolation is a design problem, not a setting
With a single agent you never notice. With several agents sharing a memory store, context bleeds: agent A starts "remembering" facts only agent B was ever told. It's subtle, it's intermittent, and it erodes trust fast — especially once different agents serve different users or departments.
