Most agents are "state blind". I built an orchestration layer with a synthetic visual tree to give agents actual Episodic Memory (LanceDB + Postgres).

I’ve been building Atom (https://github.com/rush86999/atom), a self-hosted orchestration platform in Python/FastAPI.

(Full disclosure upfront: I designed the state machines and memory architecture, but I heavily used Cursor, Aider, and Claude Code to accelerate the boilerplate and test coverage. I use API providers for the LLM reasoning, but the memory, embeddings, and orchestration are entirely local.)

I love lightweight runtimes (like OpenClaw) for simple scripts, but they break on real business workflows (like processing invoices at my company). The issue isn't the model; it's State Blindness. Agents fire a tool call into the void and hallucinate success because they have no deterministic way to verify if the UI actually changed.

Dumping raw DOMs blows up the context window, and passing screenshots is incredibly token-wasteful. So I built this to handle state explicitly:

Synthetic Grounding (Canvas AI Accessibility)