# Fix the context & data layer (agent memory)

> Cut My AI Spend — rank #1 of 10. Typical savings: Up to 90% (10x cheaper runs). Effort: Low — connect your data, agents remember it.
> Vendor: Mitosis Labs (https://mitosislabs.ai?utm_source=cutmyaispend&utm_medium=organic&utm_campaign=methods)

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

1. Inventory where your agents repeatedly re-fetch the same context (inbox scans, doc re-reads, CRM lookups).
2. Connect those sources to a memory/RAG layer once — e.g. Mitosis Cortex syncs email, docs, chat and exposes a query API.
3. Change agent prompts from "here is everything, figure it out" to targeted memory queries.
4. 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.


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