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ZDNET's key takeaways

Token-maxing to support agentic AI is unsustainable.
Business leaders must create a strategy for token use.
Give people room to explore agents within guidelines.

Twelve months in AI feels like an eternity. Steve Lucas, CEO at integration technology specialist Boomi, worried last year that CIOs were diving into gen AI projects without clear plans. Now, a year later, he sees IT teams rushing similarly into agentic tools, with token consumption climbing steadily higher.

"Whether you work inside or outside a company, it feels like your core hustle is to use AI and you token max the heck out of that technology for your own job," he said.

A token serves as the basic unit of information handled by an AI model, and studies highlight the rising, unpredictable expenses tied to agents that process far more tokens than earlier systems.

Recently, token leaderboards that once celebrated high usage within organizations have faded because teams can no longer afford wasteful spending. In the agentic era, token maxing signals excess, and tokenomics - the practice of measuring, pricing, and managing token consumption - has become essential, even for tech leaders like Lucas who observe faster token burn rates.

"A year ago, we weren't talking about tokenomics," he said. "But last year, I personally spent at Boomi 10 times the amount on Claude that I did the previous year - 10 times; that's not sustainable. I can't do that every year."

The focus now falls on companies and professionals to build their own enterprise-ready tokenomics. Agentic AI promises to reshape operations, making cost-effective exploration of agents an immediate priority, Lucas noted.

"What matters in the enterprise is ultimately the economics of AI," he said. "Most organizations will look to AI as the enterprise engine of the future. So, what matters now is, 'Can I operate AI at a return?' That is the fundamental question."

Business leaders advise giving teams guidelines and context for cost-effective model choices rather than limiting agent use outright.

Letting people shine

Snowflake CEO Sridhar Ramaswamy noted rising token use even within his firm, yet stressed the need to allow staff flexibility in testing agents.

"Are we worried about how much we are spending on AI inference across our different internal teams? Absolutely," he said. "But do I see that spend as a reason not to use AI? Absolutely not."

While token maxing raises flags, Ramaswamy explained during a media session at his firm's recent Summit 2026 event in San Francisco that creative AI applications can open fresh business paths.

Agents can improve staff efficiency and output, helping generate new client services more quickly and effectively.

"We look at agentic AI as an opportunity to optimize not just what we do, but how we can turn a process into a product that our customers can use," he said.

Like Ramaswamy, Matt Luizzi, VP of analytics at wearable technology specialist Whoop, said his firm invests in agentic tests to gain an edge.

"If we want people to push themselves out of their comfort zones, we're going to need to be OK with them taking risks and understanding that you can't break anything."

In short, Luizzi said he supports staff spending on agents without letting token use spiral out of control.

"We have guardrails in place, and monitoring and observability to let people know what they're spending," he said during a panel session at Summit 2026.

"But more likely than not, we just need to sit down and enable them on, 'How could you be doing this more efficiently and what are you trying to accomplish?'"

Putting everything into context

The path to agentic success, Luizzi suggested, lies in careful enablement. Allow teams to test agent and token limits, but prevent excess.

"At the end of the day, we're leaning in hard because we think there's an ROI to this. We are seeing people become more efficient. We are seeing work get done faster," he said.

"Those positive results mean we're not trying to be super keen on how many tokens people use but also understanding that this is something that needs to scale, so not enabling people to go crazy either."

Sriram Sitaraman, CIO at technology specialist Synopsys, said in the same panel that teams need freedom to explore new paths, including agents, to find fresh solutions: "You can't innovate with constraints; you can only go so far."

Like Luizzi, Sitaraman said guidelines help set firm limits on token use. He noted that proper project context creates effective boundaries.

"If you ask an agent, 'What should I do tomorrow?' it might consume a whole lot of data, a whole lot of tokens, and come back with something that isn't useful," he said. "If you set a context, such as creating a North America sales ops agent, giving the project specific objectives and data, then the consumption is going to be limited, and the value that it delivers to the user is higher."

That emphasis on business context was echoed by Francois-Xavier Pierrel, group chief data and ad tech officer at French TV network TF1, who told ZDNET that practical steps work best for controlling token use.

Pierrel likened unchecked use to a sugar habit. Once you start adding sugar, you adjust and risk an unhealthy dependence. AI carries similar dangers.

"Everything was cheap, very cheap, and we got all into it," he said, recalling the early push into generative AI tools. Now, agent focus brings higher costs.

"The number of tokens used can quickly become a big number. And at some point, if we look at all the companies investing crazy money into agentic AI, the bill will come back."

Pierrel said caution guides his organization, with an example of how teams weigh options for large language models.

"For use cases that are very narrow or have strong boundaries, we say, 'Can we use a small model, something that will consume fewer tokens than expected or planned, and that will still do the same job?'"

As firms roll out more agents in workflows, Pierrel said this tokenomics approach will prove vital for value creation.

"I think we are going to be more agile on this one, trying stuff, and then adapting to the use case to avoid having crazy bills," he said. "We're not shooting a rocket to the moon. So, let's be reasonable - don't deploy a million agents if you can do it on a smaller scale and it will still work."