Lower Cost, Higher Trust: The New Economics of Agentic AI

How the Denodo Platform optimizes agentic AI token consumption while improving trust

Enterprise AI is moving from assistants that answer questions to agents that reason, retrieve, call tools, and act. In these workflows, token usage does not merely accumulate; it compounds. Every additional piece of context an agent carries forward adds cost. AI success increasingly depends on optimized token usage—giving agents the right context in the most compact and governed form possible.

Connecting agents directly to fragmented data platforms results in structural token waste:

• Duplicated Discovery: Agents search platform after platform, accumulating tool definitions, schemas, and failed probes.
• In-Context Compute: Raw rows are hauled into the prompt so the LLM can perform joins or aggregations that a query engine should perform.
• Retry Loops: Ambiguous schemas and inconsistent definitions trigger costly self-correction cycles.
• Over-Retrieval: Irrelevant or unauthorized data enters the context window, increasing both cost and risk.

Instead of asking each agent to discover, retrieve, join, filter, and reconcile data entities source-by-source, Denodo provides a unified data and context layer known as "active context". Denodo delivers one governed request path, pushes the work into the data layer, and returns compact answers.

Without Denodo, token cost tends to scale with agents' tasks x sources. With Denodo, the data-access portion of token cost can scale more closely with business-ready answers delivered.

Ready to see the math? Download the full whitepaper to explore our complete four-pillar ROI modeling framework —including an illustrative example showing an 85.7% reduction in avoidable data-access tokens.