It's make-or-break for AI agents in 2026 – failure now could set adoption back years

Investors are simultaneously pricing two entirely contradictory AI narratives into the market, which is making me rather dizzy. The first is that a swelling AI bubble is about to burst, with big tech pouring hundreds of billions of dollars into a financial black hole that will one day consume the entire US economy.

The second is that AI is so consequential that it will lead to the extinction of legacy software as a service (SaaS) as we know it, with AI eradicating companies from Salesforce to SAP and everyone in between.

A dangerous combination of excitement and fear now has us wildly oscillating between frothing-at-the-mouth rallies and temper tantrum sell-offs anytime a hyperscaler or chipmaker blinks. Against this backdrop, where do today's agentic AI projects stand?

Although the jury's out on how effective they'll be, the spirit of experimentation is being ditched. Patience is wearing thin, and businesses are pushing for faster returns on AI investments following years of hype. A febrile atmosphere is spreading across the industry, and failure to deliver immediate returns could consign this technology to the same fate that previous technologies endured. Unless we can measure material success of some kind, it could set agentic AI back years.

The utopian vision of agentic AI

Wouldn't it be great to have at your disposal a fleet of bots that can act autonomously to make your work life easier and raise your productivity levels? That's the promise of agentic AI. These hypothetical AI systems go beyond the predefined constraints of large language models (LLMs) as we know them and show a level of autonomy and goal-driven behavior to help you complete tasks. That's the idea, at least. The reality is a little different.

A landmark MIT study in 2025 found that 95% of generative AI projects are failing to reach the production stage. That's partially because most companies were still treating it like traditional software – with not as much time devoted to testing. But that hasn't stopped OpenAI, Anthropic and many others from 'flooding the market' with agentic AI solutions.

With AI developers needlessly overhyping this technology, it wouldn't surprise me if we begin to see parallels with technologies like augmented reality (AR), which never really took off because the messaging couldn't match the cold, hard reality of real-world use cases. That's not because the aforementioned companies won't eventually prove the utility of AI agents, but because of the negative connotations and stigma tethered to them.

Timing is crucial

"I may have been early, but I'm not wrong," voices Christian Bale, playing the investor Michael Burry, in The Big Short.

Burry has, for months, been sounding the alarm bell on what he describes as the false economics of AI. Although his doomsday scenario hasn't yet played out – with big tech companies pledging record levels of capital investment into infrastructure and energy production – his thesis may yet be proven correct. Wonky timing, however, is not a luxury that the purveyors of emerging technologies can afford – not matter how right you might be.

The advent of 5G represented a sea change from the age of 4G, but the revolution we were promised never materialized. We can weave similar narratives about the blockchain, or smart glasses, or the metaverse. The history of AI is also a cautionary tale, especially considering the AI winters that struck first in the mid-70s and then again between the late 1980s and 1990s. Now in 2026, although neural network-powered large language models (LLMs) – and the agentic AI fabric that's layered on top of this core architecture – are improving steadily from one generation to the next, depending on which benchmarks and use cases you examine, can they keep up with expectations?

Is agentic AI a false dawn?

We can see early warning signs that today's AI hype cycle could end in something resembling a third AI winter. Friends tell me comical stories about their organizations adopting fleets of AI agents without the business actually having a plan on how to deploy them, or what core problems they are trying to solve. We've also heard OpenAI cofounder Andrej Karpathy describe the output of AI agents as "slop" adding, "they’re cognitively lacking and it’s just not working". Gartner claims that from thousands of products on the market, just 130 are authentic agentic AI services.

Dataiku research, meanwhile, found that just 23% of CIOs in the UK say they can monitor all their AI agents in real time. A majority (85%) also say that they feel more board pressure than in 2024 to demonstrate a material ROI. A similar proportion of CIOs (84%) also agree that employees are creating AI agents and apps faster than IT can govern them, with 83% concerned this could expose sensitive company data.

Agentic AI is a fantastic proposition, and there are plenty of exciting use cases. But with IT budgets increasingly financing this technology, and nervousness rising across the wider industry as investors debate the significance of AI and its impact on the global economy, the companies pushing these products must tread carefully. Following years of hype, even the slightest of fumbles could spread more panic – and eventually consign agentic AI to the same fate suffered by plenty of innovations burdened with needless hype, and eventually ditched by the business world.