AI Strategy
The Data Layer: Why AI Agents Need More Than a Prompt
An agent is only as accurate as the context it can reach. The data layer — your lake, your systems of record, and the MCP servers that broker access — is what separates a useful agent from a confident hallucination.
Most failed AI pilots are not model failures. They are context failures. The model is fine. It just had no way to see the data it needed to answer the question correctly.
Hallucinations are usually a symptom of starvation. When an agent can't reach the system of record, it guesses. A guess wrapped in fluent prose is the most expensive kind of wrong answer because nobody catches it.
The fix is a real data layer. That means a lake your agents can query, systems of record they can reach through governed connectors, and a protocol — MCP is becoming the default — that lets any model use any tool without bespoke glue code per integration.
Universal context access is the unlock. The same agent that drafts a customer email needs the customer's last ticket, the open invoice, and the contract terms. Without that, it's a fancy autocomplete. With it, it's an analyst.
Dataken's platform is built on this premise. OLi observes work in flight and stitches together the activity graph that other agents draw from — so the model isn't guessing what's happening in the business, it's reading from a substrate that already knows.
Key takeaways
- Most hallucinations are context failures, not model failures
- A real data layer = lake + systems-of-record connectors + a protocol like MCP
- Agents need governed access to operational data, not just documents
- Universal context access is the difference between autocomplete and analyst-grade output
- The activity graph is the substrate other agents draw from