As enterprise AI evolves from assistants that answer questions to agents that plan, retrieve information, call tools, validate results, and take action, token economics become more complicated, and therefore more important to manage.
A conventional chatbot might process a prompt and simply generate a response. However, an agentic workflow might make multiple model calls to complete a single task. At each step, the model may receive prior conversation history, retrieved data, tool descriptions and outputs, intermediate results, and error messages. If unnecessary context enters the workflow early, some of it may be carried forward and processed repeatedly.
This means that token consumption does not always grow in a simple, linear way. A modest amount of over-retrieval at the beginning of a multi-step task can become much more expensive by the end. The answer is not to deprive agents of context. Agents need sufficient, current, and trustworthy information to act reliably. The objective is to provide the right context at the right time, in the most compact and governed form possible.
Token Efficiency Starts Before the Prompt
Much of the discussion about AI cost focuses on model selection, prompt length, caching, summarization, and compression. These are important considerations. But they primarily help manage context after an AI application has assembled it.
Enterprises should also examine how that context is produced.
The data of most organizations is distributed across lakehouses, warehouses, operational systems, SaaS applications, documents, vector stores, APIs, and third-party sources, and they use different schemas, business definitions, access methods, and security policies. When agents navigate such an environment source-by-source, they can incur a substantial “token tax” before they even get to answering the business question.
That tax generally appears in four forms:
- Duplicated discovery: The agent searches multiple catalogs, evaluates numerous tool definitions, inspects schemas, and attempts different access paths for overlapping datasets, unnecessarily consuming tokens.
- In-context computation: Raw records are brought into the model so the LLM can compare, join, filter, or aggregate them. This is work that can consume many tokens and is better suited for deterministic data engines to perform, essentially for free.
- Retry loops: Ambiguous schemas and inconsistent business definitions lead to failed queries, corrections, and repeated retrieval, consuming additional tokens on each retry.
- Over-retrieval: Irrelevant, restricted, or unnecessarily detailed data enters the context window, adding both token costs and potential security and regulatory exposure.
Model Context Protocol (MCP) can standardize how agents interact with tools and data, but it does not eliminate this architectural problem on its own. If every source or domain exposes a separate MCP server, organizations may recreate integration sprawl at the agent layer. The servers must be secured, governed, documented, monitored, and explained to the agent — and their tool and schema descriptions can themselves consume context.
Give Agents Answers, Not Raw Enterprise Data
The Denodo Platform addresses token economics upstream, by reducing how much work and how much raw data the AI model needs to carry in its context window.
The platform provides an active context layer across the distributed enterprise data estate. Rather than requiring agents to discover, retrieve, reconcile, and govern data independently for every task, it provides a logical layer that delivers compact, business-ready, policy-compliant answers through a consistent interface.
Four capabilities work together to make this possible:
Universal Connectivity Reduces Repeated Discovery
Agents can discover and access distributed data through one logical layer instead of navigating each platform, catalog, API, and store separately. This reduces source-specific tool context, redundant schema inspection, and failed probes while avoiding hard-wired access logic for each use case.
Zero-Copy Live Data Access Pushes Computation Down
Filters, joins, and aggregations should usually be deterministically performed by query engines, not probabilistically inside an LLM. Denodo can push this work to underlying systems or execute it in the logical layer, returning a compact result rather than large raw payloads. Agents gain live situational awareness without requiring every dataset to be copied into a separate AI repository.
Unified Semantics Reduces Retries
Terms such as “customer,” “active account,” “net revenue,” or “risk exposure” often mean different things across systems. Shared business definitions, relationships, and source-of-truth logic, all enabled by the same logical layer mentioned above, help an agent form the right query the first time, reducing trial-and-error retrieval and self-correction loops.
Centralized Governance Filters Context Before Delivery
Governance is not only a risk control; it can also be a cost control. Row- and column-level security, masking, and policy enforcement can exclude irrelevant or unauthorized information before it enters the model context, and Denodo enables all of this by design. The most secure token is the one that never reaches the prompt, and it is also the least expensive.
These four capabilities are mutually reinforcing. Connectivity without semantics may merely expose more confusing data. Semantics without live access may provide the right definition but stale information. Access without governance can increase risk and over-retrieval. Token optimization is therefore an architectural outcome, not a single product feature.
An Economic Model for Token Consumption
To make this discussion actionable, organizations need more than a general claim that better data architecture should lower AI costs. They need a way to identify where avoidable tokens originate and estimate how architectural changes could affect consumption.
Our new white paper introduces a directional economic model for the data-access portion of agentic AI workloads. It begins with core workload inputs such as monthly agent tasks, the number of subsequent turns in which retrieved context persists, and the blended token price. It then examines the token contribution of each active context pillar:
- How many sources an agent must search and how much discovery context each requires
- How much raw data is retrieved versus the size of a compact, optimized answer
- How frequently ambiguous requests trigger retries and the resulting cost
- How much irrelevant or unauthorized data is retrieved versus what remains after policy filtering
The model compares a fragmented, source-by-source access pattern with a unified, governed approach. Its purpose is not to promise a universal savings percentage. Actual outcomes depend on agent design, data volumes, model choice, caching, prompt strategy, retrieval behavior, and other implementation details. Instead, it gives data and AI leaders a structured way to develop assumptions, test them against their own workloads, and focus optimization efforts where they matter most.
The broader economic shift is straightforward: without a common data and context layer, the data-access portion of token consumption can grow with the number of agents, tasks, sources, records retrieved, and retries. With a unified approach, it can scale more closely with the number of useful, business-ready answers delivered.
Trust and Efficiency Share the Same Architecture
The practices that reduce avoidable token consumption also make agentic systems more reliable. Live access improves situational awareness. Shared semantics improve interpretation. Pushdown computation produces precise, compact results. Centralized governance makes answers respect policy.
So the most useful question may not be, “How can we pay less for the tokens our agents consume?” It may be, “Why are those tokens being generated in the first place?”
For a deeper technical discussion and a practical model for estimating the token impact across your own agentic workloads, download the white paper, Lower Cost, Higher Trust: The New Economics of Agentic AI.
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