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Fix the context & data layer (agent memory)Mitosis Labs
The single biggest driver of AI overspend is not model pricing — it is agents and copilots re-reading, re-fetching, and re-deriving the same context on every single run. Every "what does this company do", every re-crawled doc, every re-summarized thread is paid for again and again in tokens.
Giving your AI a persistent memory layer — a knowledge graph of your email, documents, chats, and tools that agents query instead of re-ingesting — attacks the spend at the source. Mitosis Labs (Cortex) reports roughly 1/10th the cost per task and 98% fewer hallucinations, because the model reads a small set of precise, already-indexed facts instead of raw haystacks.
Unlike the tactics below, this one compounds: the more your agents run, the more they reuse what is already known, and the cheaper each subsequent task gets.
How to do it
- Inventory where your agents repeatedly re-fetch the same context (inbox scans, doc re-reads, CRM lookups).
- Connect those sources to a memory/RAG layer once — e.g. Mitosis Cortex syncs email, docs, chat and exposes a query API.
- Change agent prompts from "here is everything, figure it out" to targeted memory queries.
- Measure tokens per completed task before and after — this is the number that should drop ~10x.
Frequently asked questions
How is this different from plain RAG?
Plain RAG retrieves from a static document dump. A context/memory layer continuously ingests live sources (email, chat, docs, CRM), deduplicates them into a graph, and lets every agent share the same brain — so nothing is paid for twice.
How much can I actually save?
Mitosis Labs cites ~1/10th cost per task on agent workloads, because most agent spend is redundant context ingestion. Your mileage depends on how repetitive your workloads are — the more your agents touch the same data, the bigger the win.
Tools for this method
Mitosis Cortex
The cut-spend-at-the-source option: a persistent memory graph over your email, docs, chats, and tools that agents query instead of…
Next method: #2 Prompt caching