# The hidden economics of AI context > Source: > Published: 2026-09-23 17:17:04+00:00 Over the last three decades, the major innovations in enterprise tech have focused on scaling the infrastructure. From VMs, to containers, to big data, to supporting millions of concurrent users on web and mobile applications – the focus was evolving distributed systems to handle more traffic and data, faster, without falling over. What we’re seeing today with agentic AI is different, because it changes what is scaling. Previous infrastructure waves were largely about handling more data, traffic, and interactions for human users. In the AI era, it’s the number of agents doing the work, and the resources they consume, that are scaling. Employees who once completed individual tasks themselves will increasingly orchestrate tens or hundreds of agents, leaving large enterprises to manage thousands or even millions of autonomous workers acting on their behalf. That shift changes the economics of enterprise technology. Traditional cost controls could tell a CIO that a budget is on track to be exhausted ahead of the next budgeting cycle. But at agent scale, autonomous systems can consume resources faster than traditional cost controls have time to react. Instead of simply imposing a cap once a budget threshold is reached, organizations need ways to reduce unnecessary consumption while the work is happening. That begins with understanding what agents consume, and why the quality of the context they receive affects more than just the token bill. [Tokenomics](https://www.finops.org/insights/token-economics-the-atomic-unit-of-ai-value/) is more than a model billing problem. The real economic levers are in the data layer. [Context layers](https://cloud.google.com/products/knowledge-catalog) do more than ensure accuracy and avoid hallucinations; they’re the primary determinant of agent efficiency. Giving agents more context than they need creates unnecessary reasoning loops and additional calls to data processing, storage, and network infrastructure. Relevant and succinct context reduces latency and cost throughout the stack while helping to ground agent reasoning in a single source of truth. The economic discipline behind context engineering is all about giving agents the smallest set of high-value information needed to complete the task. But manually constructed ontologies decay quickly, often becoming outdated before they’re deployed. Agents need a unified context layer that spans clouds, applications, and systems, continuously adapting to the underlying data and business logic. Every wasted reasoning loop cascades costs with calls down into the infrastructure beneath the model. A single agent task triggers pipeline queries, cross-cloud network calls, and storage hits. Focusing only on tokens misses the big picture of where most of the bill actually accumulates. For CIOs, the real economic measure is total cost of ownership. Token usage must be understood alongside the compute, storage, networking, and data access required to produce an outcome. This becomes especially important because agents need access to [structured and unstructured](https://cloud.google.com/bigquery?e=48754805#unstructured-data-analytics) information distributed across clouds, software services, and the broader data estate. Once autonomous systems begin reaching across that estate at scale, controlling the average cost of an interaction is not enough. Enterprises also need to know whether those costs can be predicted. Predicting costs is much harder when agents have to reach across a distributed data estate. An individual request may involve inference, retrieval, network traffic, and several underlying services. At agent scale, that variability makes financial planning extremely challenging. [Cross-cloud data access](https://cloud.google.com/products/lakehouse) is one example of this. Moving data between hyperscalers has long exposed enterprises to egress fees that can make a valuable workload economically impractical. Lowering that cost matters, but converting it into a stable monthly commitment is just as important. An IT leader needs predictable price-performance at scale, ensuring autonomous workloads remain governable without risking runaway costs. Measuring AI progress by tokens consumed is like measuring developer productivity by lines of code – it’s a misleading and unhelpful metric. The golden signal on how well you are doing in AI economics is how well you’re delivering business value through the most efficient possible resource consumption. That doesn’t mean enterprises should limit experimentation before they understand what the technology can do. Missteps are a part of learning in any big technology shift. But experimentation must eventually lead to operating discipline. CIOs should find focused use cases, invest in the [data foundations](https://cloud.google.com/data-cloud) they require, track the business value, and scale what works. The organizations that get this right will be the ones who can measure what every agent, and every token it consumed, actually delivered. To learn how leading enterprises are connecting their agentic AI to trustworthy, enterprise-wide data, read the [MIT Technology Review Insights report](https://cloud.google.com/resources/content/scaling-ai-agents-trustworthy-data?e=48754805&utm_source=cio_idg&utm_medium=editorial&utm_campaign=FY26-Q3-GLOBAL-STO558-website-dl-mit-sclngagnts-229374&utm_content=context&utm_term=cio).