This post is for CIOs, CTOs, and VPs of engineering who are ready to transition from AI agent pilots to full production deployments, as well as CFOs and FP&A leaders building AI expense forecasts they haven’t had to build before.
Agentic AI costs have two layers. The first is a fixed cost that you may pay monthly or annually, just as you would for any software as a service (SaaS) subscription. Because this number is predictable, it’s easy to budget around it.
Unfortunately, many organizations stop there without considering the second level of AI agent pricing. Much less predictable, this level is based on how many AI tokens or credits your organization consumes.
Every model call, document retrieval, application programming interface (API) action, and follow-up loop generates a charge. A single agent task can include dozens of actions, each with its own cost, and when agents create their own agents, consumption goes higher. That makes the consumption layer variable and difficult to forecast.
In other words, a team that adds five agents adds five variable factors to your budget, not five predictable line items. Your goal is to establish governance around how those five agents work and the people who create them to reduce the variability.
In our blog post “AI Agent Governance With M365,” Microsoft Practice Lead Karina Myers compared onboarding new agents to onboarding new employees:
“Picture a new hire who started last Monday with no manager, no access request, and no controls keeping them from reaching every file in your environment. . You would flag that employee as a security risk on day one, and the employee would not make it to day two. Unfortunately, that’s how many AI agents show up to work, but they will likely go unnoticed.”
That principle applies here, too.
For example, if you hired five new employees, you wouldn’t hand each of them a company credit card and send them on their way. Instead, you’d set expectations, watch spend, and step in when something looked off. Agents need those guardrails, too.
Many organizations have been put into a financial bind by not considering the variable layer, which has led them to cancel agentic projects. In fact, Gartner predicts that more than 40 percent of agentic AI projects will be canceled by the end of 2027.
Some try to combat the spending trap by buying a bundle based on their number of users, but this doesn’t control consumption. As a result, costs can creep above acceptable limits — especially if you don’t have visibility into your organization’s agents.
To control agentic AI costs, you must budget the meter, not just the license.
The first step toward cost control is understanding that agents are not right for every task. For that reason, you must start by carefully choosing your AI agent use cases before you start building.
Even when teams are tempted to build an agent, a deterministic script can often do the same work. Or a person may already have deep knowledge of the context to perform a task. Handing parts of the task to a human may be more cost-effective than having an AI agent build a comparable context (though you should also ensure that person’s knowledge is documented).
This is not a human-versus-agent argument. Rather, it’s an exercise of matching the right tasks to the right solutions. The practical approach we use at Centric Consulting starts with understanding your business problems before anyone starts building an agent.
To determine whether an AI agent is the right solution for a particular problem, we ask questions such as:
If the answer to these questions is yes, you have a strong case for investing in AI agents. Otherwise, you may want to explore non-agentic AI or other approaches, such as robotic process automation (RPA).
If you decide to invest in agentic AI, you must ensure the agent cost is visible.
You can’t manage spend that you can’t see. That’s why you also need visibility into your spend before you start generating agents at scale.
The discipline is the same on every platform: Attribute spend to a team, an agent, and a specific use case. If you create a vague “AI” or “innovation” line item into your budget, your costs will be invisible.
Many major platforms come with cost reporting, so it’s not hard to find a dashboard with useful data. The real challenge is a financial operations challenge, and it involves deciding what to attribute and who owns each number. It’s equally important to decide what happens when spending crosses a threshold.
To set up a useful agent attribution system, you should:
Cost observability also connects directly to agent governance. You must know what agents exist, who owns them, and what they’re doing. This is what makes spend attributable in the first place. AI governance and visibility work together by examining cost factors from different angles.
When you put attribution and ownership ahead of volume, you embed cost consciousness in your governance and team culture.
Organizations are under tremendous pressure to build AI agents and agentic workflows, but few understand the complexities of agentic AI pricing. Failing to consider the consumption costs, whether measured in tokens or credits, can wreck your carefully crafted and approved AI budget.
However, the impact may go far beyond the balance sheet.
Adnan Masood, chief AI architect at AI firm UST, told IBM regarding compute costs: “We’re entering a strategic inflection point where innovation — once viewed as a competitive necessity — now carries substantial financial risk. We’re looking at a future where companies must make strategic bets on whether to continue pushing the boundaries of AI, or risk falling behind…in the AI arms race.”
Before making your bets, step back and consider whether AI, agents, humans, or scripted intelligence are best suited for your tasks. Then, put agent visibility in place so you know where and how your tokens or credits are being used.
With these two strategies, you can control your agentic AI costs and set yourself up for a more innovative — and responsible — AI future.
Are you ready to transition from AI agent pilots to full production deployments but aren’t sure where to start? Our AI consultants can guide you through the entire process. Let’s talk.
