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As agentic AI moves into production, controlling LLM cost and reliability starts with controlling the context delivered to the model. AI Context Optimizer™ transforms enterprise data into precise context for each task, reducing token consumption while improving governance.
Ahead of the Gartner Data & Analytics Summit in Orlando, which opens today, we’re excited to introduce AI Context Optimizer™, our new product for making agentic AI more scalable, more cost-efficient, and easier to govern in the enterprise. Gartner’s Orlando summit runs March 9 to March 11, 2026, making this the right moment to talk about a challenge many data leaders are starting to feel very directly: LLM cost is becoming a real architectural issue, not just a usage metric.
Why agentic AI changes the data challenge
As enterprises move from AI experiments to production-scale agentic systems, the problem changes. It’s no longer enough for data to be AI-ready. It also has to be optimized at the moment of LLM inference. That’s because agentic systems do not behave like traditional applications. They reason through tasks in multiple steps. They interact with multiple systems. They make repeated queries. And too often, they push far too much enterprise data into the model as context.
That creates a new kind of drag on production AI. Token consumption rises fast. Latency rises with it. Costs become harder to predict. Reliability can suffer when models are forced to reason over excessive or irrelevant context instead of the precise information needed for the task at hand. For enterprise data leaders, token usage is quickly starting to look like a new cloud bill.
Introducing AI Context Optimizer
That’s exactly the problem we built AI Context Optimizer to solve.
AI Context Optimizer autonomously generates optimized AI tools that retrieve and convert enterprise data into precise context for each agentic task. Instead of sending large volumes of raw enterprise data to the LLM, these tools deliver only the context the model actually needs. The result is lower token consumption, lower inference costs, faster response times, and tighter control over how AI agents interact with enterprise data.
Just as important, this is not only about efficiency. It’s also about discipline. The optimized tools generated by AI Context Optimizer embed governance directly into the interaction layer. Agents are restricted to the scope of the specific task and business entity involved. Sensitive data is dynamically masked based on user roles and permissions. Actions can be limited to what the agent is authorized to perform. In other words, we’re not just reducing cost. We’re shaping how enterprise data is delivered to AI in a way that’s more precise, more controlled, and more enterprise-ready.
Moving beyond AI-ready data
We see this as the next step beyond AI-ready data.
For some time now, we’ve helped enterprises make data AI-ready through governed data products. But agentic AI raises the bar.
Now the challenge is not just preparing data for AI.
It’s transforming enterprise data into precise context at inference time. That’s the shift. And that’s where we believe the next wave of AI architecture will be won or lost.
See it live at Gartner Data & Analytics Summit
We’re looking forward to showing AI Context Optimizer live this week at Gartner Data & Analytics Summit in Orlando. If you’re attending, come visit us at booth 116 and see how precise context can lower LLM costs and make agentic AI more economically scalable in production.
