Wanted: Token spend control and governance without slowing down agentic AI innovation

Date:
Wed, 26 Aug 2026 14:32:28 +0000

Description:
Agentic AI is accelerating, but organizations need stronger governance to control costs, security and access.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Scary AI spend and weak access security controls are the flipside of the sharp rise in agentic AI . The answer lies in the form of a control plane that provides the visibility CIOs need.

The past couple of years have seen enterprises press the fast-forward button on generative AI strategies. e have all seen a remarkable change in
deployment patterns from useful chatbots to autonomous agents that engage deeply with core software, data and LLMs, and even create their own offspring agents. CIOs and others are rightly excited about the capacity of agentic AI to reduce manual processes and accelerate speed to insight and actions.
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However, governance and AI token costs are alarming challenges. Sammy
Zoghlami Social Links Navigation

SVP EMEA at Nutanix. Whats needed today then is the power of AI without the major concerns over incurring access control risks relating to Shadow AI and agent sprawl, and without the bill shock relating to unmanaged token consumption. You may like The agent problem nobody budgeted for Agentic AI's crossroads: guardrails or massive fails As AI scales, is meaningful
governance possible? Moving fast The generative AI shift that has occurred so quickly spans the early days of ad hoc AI experimentation via testing and pilots to todays enterprise-grade business processes. It has also outpaced
the progress of the usual governance frameworks and effectively rendered them obsolete.

Experienced CIOs may compare the change to the early days of cloud computing platforms when staff with credit cards expensed the AWS trials they had spun up and quickly ran up staggering bills. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me
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Or they may hark back to the days when sales leaders set up online CRM platforms or other process owners bypassed IT to run their own Sharepoint systems.

Or, to reach for yet another lesson from history, they may compare what is happening with generative AI now with the advent of bring-your-own-device schemes and WiFi.

Remember when the user-led demand to have the shiny new gadget ran a coach
and horses through the command-and-control IT regimes of cloned PCs stamped with a single image and CIOs no longer managed everything that touched their systems? What to read next Why AI coding agents keep stalling before production and the governance controls that fix it Agentic AI adoption outpaces governance in regulated industries Govern AI agents before they go rogue The problem is here. Deal with it However we make the comparison, its clear that today there are very real risks relating to data leakage, compliance standards and IT costs because of the speed with which AI has advanced. Nobody wants to lose a chance to gain a competitive advantage and even to fundamentally re-engineer and automate expensive, error-strewn manual processes. So CIOs are once again being asked to square a circle.

As IDC has said: There is a well-established playbook for calculating the return on an IT investment. Define a baseline, model the efficiency gains, project the cost savings, and present a number to the CFO. For most
technology investments, that approach works. Agentic AI breaks it.

Agentic AI is not fixed and predictable so its tough to manage and calculate return on investment, but we know it can take out swathes of cost, so we have to use it and we have to manage costs as best we can.

Gartner has already suggested that by 2027, more than 40% of agentic AI projects will be cancelled due to factors such as cost overrun fears or inadequate risk management. Thats not good enough. Corporate IT must step up and deliver the value the business needs, just without the attendant risks
and costs.

The solution here is to head off undesirable outcomes at the pass by
providing a check, a little like an airport security system that identifies passengers and scans for any potentially unsafe activity or nearby environmental factors such as possession of sharp objects.

You will see this happen in IT very quickly now, such are the risks and speed of what is happening. If 2026 is the year of agentic AI, then it also has to be the year of secure agentic AI. Enter the AI gateway One way to address the challenges outlined above is to create a gateway or filter. This approach offers a single control layer regardless of whether the AI model is hosted in public or deployed as a private, self-hosted entity. It effectively secures traffic from agents to LLMs and from MCP servers. This in turn enables organizations to have centralized policies and a clear view of token accounting.

It even has the visibility impact of informing decisions of non-techies who know little about AI operations. So, for example, a CIO working in tandem
with a CFO perhaps may identify a better cost efficiency (for example, switching from a hosted model to a private model) and act accordingly.

Thats great news for CIOs who want to maintain a reputation for IT being agents of change but not its less desirable reputation for being risky and pricey. Looking for the best cloud storage? These are our top picks. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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