Listen to "Lower Cost, Higher Trust: The New Economics of Agentic AI" on Spreaker.
Welcome to another episode of AI Data Bites (Powered by Denodo). As AI rapidly moves beyond simple chatbots into autonomous agents that reason, retrieve information, and take action, enterprise data management is facing a fundamental shift. From merely managing data to controlling the "cost of intelligence" and enabling trusted execution. This podcast is dedicated to exploring the architectural foundations, token economics, and governance required to power successful enterprise AI initiatives.
To kick off Part 1 of our series on the economics of agentic AI, host Neha Gurudatt is joined by Dominic Sartorio, VP of Product Marketing at Denodo, to discuss key insights from the whitepaper "Lower Cost, Higher Trust - The New Economics of Agentic AI." Together, they break down why multi-step agentic workflows cause token costs to grow exponentially and how an Active Context Layer can help organizations build cost-efficient, production-ready AI.
Key Insights from This Episode
- The Token Economics Challenge: Learn why token usage and costs multiply exponentially when agents perform repeated reasoning, retrieval, and tool validation steps.
- Overcoming Enterprise Data Complexity & "Token Taxes": Discover how an Active Context Layer shifts token consumption from scaling with data sources to scaling directly with business outcomes.
- Avoiding "MCP Sprawl": Explore how Universal Connectivity, Zero-Copy Data Access, Unified Semantics, and Unified Governance optimize prompt context upstream, preventing unauthorized data access while drastically lowering computing overhead.
- Modeling Up to 85% Token Reduction: Discover findings from Denodo's ROI framework. In modeled scenarios of 500,000 monthly agentic tasks, an Active Context Layer reduced avoidable data-access token usage from 42.6 billion to 6.1 billion, delivering an ~85.7% token reduction alongside higher security and speed.
Why This Episode Matters
The future of enterprise AI will not be determined by model sophistication alone. It will be determined by whether organizations can build a smart architecture behind those models to deliver relevant, governed, and optimized context without runaway costs. As agentic AI enters live business workflows, token inefficiencies act as a direct tax on scaling. This episode provides an actionable roadmap for enterprise leaders to eliminate token tax traps, improve data accuracy, and achieve lower costs with higher trust across their AI landscape.
🎧 Join us for this premiere episode of AI Data Bites (Powered by Denodo) and discover how to master the new economics of agentic AI.
Visit the AI Data Bites podcast page to catch future episodes throughout Season 5.
