The data foundation for agentic AI
Agents fail at the foundation, not the model. This paper makes the case for a single multi-model engine that holds context, memory, and meaning in one transaction, names where that case does not hold, and shows where to begin.
Key takeaways
The 2026 reality: the data foundation, not the model, is the bottleneck for agentic AI.
What an agent actually needs from data - and the three things people confuse: context, semantics, and knowledge.
The architectural argument for atomic context: one multi-model engine that holds context, memory, and meaning in a single transaction.
Why the fragmented stack falls short, drawn fairly against the landscape - including the honest trade-offs and limitations.
Memory as the missing half (Agent Memory), what this enables, and where to start.
What's inside
- Executive summary
- The 2026 reality: the foundation is the bottleneck
- What an agent actually needs from data
- Three things people confuse: context, semantics, and knowledge
- Why the fragmented stack falls short
- The architectural argument: atomic context
- Memory is the missing half: Agent Memory
- The landscape, fairly drawn
- Trade-offs and limitations
- What this enables, and where to start
- Technical appendix: a minimal illustration
