Your AI Needs a Brain, Not a Better Model
A map of the 12 memory and RAG tools building the AI brain layer. Why temporal knowledge graphs beat vector-only RAG, and how to pick the right stack for your agents in production.
GTM AI Brain: one temporal graph for every agent
Temporal knowledge graph that gives every GTM agent the same fresh, time-aware memory.
Python Neo4j Graphiti Pub/Sub GCP FastAPI
Problem
Every GTM agent (outbound, expansion, marketing-to-sales, collateral) re-discovered the world on each run. Account research re-scraped sites it scraped last week. The expansion agent did not know the research agent had already noted a CEO change. Salesforce, HubSpot, Pendo, transcripts, usage, health scores: each in its own silo. Duplicated cost, stale context, contradictory answers.
What I built
I built a central brain on Graphiti and Neo4j 5. Entities and facts carry time validity, so the graph can answer what was true on a date and what changed since. Ingest is event-driven over Pub/Sub from Salesforce, HubSpot, Pendo, customer conversations, platform usage, and health scores. Every agent calls a query API before and during its work and writes back what it learned. A freshness layer flags stale facts for re-fetch.
What I learned
Agents need shared memory more than they need a bigger model. Once they started each run grounded in the same graph, cost dropped and contradictions went away. The write-back contract mattered as much as the read API.
External 
Substack
Substack RAG is broken: we need connected entities, not isolated chunks
Why text chunking destroys the context that matters most.
External 
Towards AI
Towards AI Drafted, not sent: how I built the second half of an AI outbound agent
A rep told me I had built half the product. So the agent now drafts the full 10-touch sequence and drops it into Outreach, ready for the rep to review and send. Nothing goes out on its own.