Most agent frameworks give each agent its own context window and call it memory. That works right...
Multi-model agent fleets rediscover facts, multiplying inference costs; unified append-only memory + scoped retrieval stops it. Narrow searchable domain by agent role before ranking—this controls cost and prevents cross-domain hallucination at 5+ agent scale.
Most agent frameworks give each agent its own context window and call it memory. That works right up
until you run more than one agent, and then it quietly becomes the most expensive design decision in
the system.
We run a fleet where different agents are deliberately backed by different models — one family
handles long-form drafting, another handles structured extraction, a couple run on a local path with
How Empire Labs Pty Ltd gave a 12-agent autonomous fleet durable memory across sessions - hot memory, state files, session…
Understanding different memory architectures for AI agents and when persistent, episodic, or vector memory actually pays off in…
After building 50+ AI systems, here is what we know about Enterprise AI Agent Orchestration with...
As someone who runs multiple AI agents for day-to-day tasks, I kept hitting the same wall: every new...
Most "agent memory" today is one thing wearing three hats: a vector database.You embed the past, you...
I've been using AI agents like Claude Code and Cursor for a while, and the biggest headache was...
