Multi-Agent Cognition (M.A.C.) is an end-to-end cognitive backbone that lets LLM agents act in the real world for days, not prompts. It fuses three hard pieces most stacks hand-wave: a neuro-symbolic control spine with microsecond-level interrupts, a seed-deterministic spatial GraphRAG navigator, and a tri-partite memory system that sleep-consolidates experience into compact, self-cleaning long-term state.

Instead of throwing bigger context windows and ad-hoc tools at the problem, M.A.C. gives your agents fast reflexes, efficient map reasoning, and compressed, auditable memory — so they stay responsive at millisecond timescales while planning over weeks of history without drowning in tokens or losing critical facts.