Five agents asking one question should cost one answer
Multi-agent systems fan out, and they ask overlapping questions constantly. Without a shared cache, that overlap is pure waste, the same answer, bought once per agent.
The moment you go multi-agent, a new cost appears that single-agent apps never see: overlap. Five agents working a problem will independently ask for the same schema, the same policy, the same definition, phrased a little differently each time. Without something in the middle, that's five model calls for one piece of knowledge.
flowchart TD A1["agent 1: 'what's the schema?'"] --> CK["crowkis"] A2["agent 2: 'schema for orders?'"] --> CK A3["agent 3: 'show orders schema'"] --> CK CK -- "1 model call" --> LLM["provider"] CK -- "2 semantic hits · ~ms" --> DONE["answers"] style CK fill:#fbe9e8,stroke:#d62221,stroke-width:2.5px
In plain words. Put Crowkis between your agents and the model, and the first agent's answer is instantly available to the rest, by meaning, so the different phrasings still match.
As you add agents, this only gets better. More agents means more overlap, and more overlap means a higher cache hit rate. The architecture that scales your costs the fastest, fan-out, is exactly the one a shared semantic cache tames the best.
The bottom line
In a multi-agent system, shared knowledge should be a shared cost. One answer, reused across the swarm, instead of one bill per agent.