Mainstream software suppliers are pursuing a “philosophy of madness” by persuading enterprises to deploy thousands of artificial intelligence (AI) agents, says Alan Trefler, the CEO and founder of Pegasystems.
Trefler, known for his outspoken views, claims that enterprises are in danger of lining up problems for the future by letting loose en masse AI agents with the ability to make potentially business-critical decisions on their computer networks.
Industry watchers say Trefler has a point when it comes to mission-critical workflows in the regulated industries Pega supplies, but that other software suppliers are getting a grip on agentic AI.
Neil Ward-Dutton, research vice-president for agentic automation and AI technologies at analyst group IDC, told Computer Weekly that while there is a race by suppliers to be seen to have AI capabilities, behind the scenes, other suppliers are introducing their own guardrails for AI.
“I think Pega realised pretty early on that you can use AI to help you create a deterministic system, like a Pega application. Now … all the other vendors are starting to do that too, but Pega was one of the first,” he said.
“When you get behind the headlines and speak to ServiceNow or Salesforce or Microsoft, they will all talk in their own way about this kind of hybrid approach, where yes, you can use AI, and you can use AI agents, but actually you need to combine them with traditional, deterministic, more rules-based approaches,” he said.
The madness of mass AI agents
Trefler told Computer Weekly that mainstream enterprise software suppliers are in danger of going down the wrong track by promoting the idea that critical systems can be safely built on AI agents.
“I think they have already acknowledged they’re going to have thousands of agents running, maybe over 10,000, and I think that philosophy is madness,” he said.
The big enterprise software suppliers are introducing “control towers” to allow enterprises to monitor and manage their AI agents.
But Trefler argues that AI agents are too unpredictable to be used at scale to run mission-critical applications in enterprises.
“[Software companies] have already acknowledged they’re going to have thousands of agents running, maybe over 10,000, and I think that philosophy is madness”
Alan Trefler, Pegasystems
“You don’t want these disaggregated, disassociated initiatives trying to run important things in the business where it might treat customers differently in ways that it shouldn’t,” he said.
When AI goes wrong, it can go wrong in a spectacular fashion, as marketers who chose an AI-generated slogan for Starbucks in South Korea found out, when their “Tank Day” campaign led to national riots and customers smashing Starbucks-branded cups, followed by the resignation of its chief executive.
Founded by Trefler in 1983, Pegasystems is now on track to become a $2bn revenue organisation.
Based in Massachusetts in the US, the software company supplies a low-code business process platform to some of the world’s largest companies. It lists Deutsche Telekom, Lloyds Banking Group and Daimler Trucks among its customers.
The AI bubble will burst
Trefler sees a big future for AI in the enterprise, but he is wary of its unpredictability and the soaring costs of AI tokens, and believes the current rate of investment in datacentres to power AI is unsustainable.
“It was very sobering to me to drive down Highway 101 in California about two months ago,” said Trefler. “There was billboard after billboard of AI companies. I’m in the business, and I didn’t know 80% of them.”
He predicts that when the AI bubble bursts, many of these companies are unlikely to survive.
“I think you’re going to get a real flash in the pan from lots of companies that may have had a good idea, but find that there’s a big step between that and sustainable business,” he said.
Trefler has lived through tech bubbles before. The fibre optic cable bubble burst in 2000 after companies laid huge amounts of fibre optic cable that vastly exceeded demand.
There was “an inevitable financial rebalancing”, with many fibre optic companies going out of business. But over the next 20 years, the excess fibre was eventually used.
If and when the AI bubble bursts, enterprises will need to re-evaluate what they want to use powerful large language models (LLMs) for, he suggests.
AI changing the nature of the IT profession
AI has already changed the nature of the IT profession. Trefler says software platforms, such as Pega’s Blueprint and Pega’s Infinity cloud platform, are increasingly allowing people without IT skills to design and build business applications.
A few years ago, the same tasks would have required experts in Pegasystems software, who were often hard to find and commanded top salaries. Today, the design work can be carried out by people with business skills rather than technical skills.
Businesses will still need technically experienced people to integrate complex IT systems, but standard integrations are just going to get done, according to Trefler. “They’re already pretty much out of the box,” he said.
Widespread use of AI means IT jobs will become “compressed”. There will be a need for fewer programmers, for example, as large language models churn out high volumes of code.
That has put IT professionals under pressure to prove their worth by using LLMs to “vibe code” computer code at high volume.
“The trouble is, we’ve all learned that having more and more code in the business doesn’t make that business more reliable,” said Trefler.
The need for predictability
Trefler describes himself as a lone “voice crying in the wilderness” and, going against perceived wisdom, warns enterprises to think twice about deploying agentic AI at scale.
He does not see how letting thousands of AI agents loose to interact with customers, or make decisions that affect the business, can be done safely or cost-effectively.
“What I am saying is that enterprises that want to have a certain consistency of process, consistency of soul, consistency of outcome, need to do it in some other way,” he said.
AI is just too unpredictable, warns Trefler. It could treat customers differently in ways that it shouldn’t, or it might accidentally violate a law or a regulation.
“You don’t want these disaggregated, disassociated initiatives trying to run important things in the business,” he said.
Over the past two months, the economic impact of AI has become a growing issue, with CEOs starting to worry about the rising cost of AI tokens.
Trefler, only half joking, compares current token price hikes to a drug dealer winning over clients by offering free or cheap drugs, only to ramp up the price when they are hooked.
The tendency to measure the impact of AI by the number of AI tokens employees consume is misplaced, in Trefler’s view, with the only credible metric being whether AI is helping businesses save money or make money.
The case for deterministic workflows
Neil Ward-Dutton, research vice-president for agentic automation and AI technologies at IDC, says Trefler is “right on the money” when it comes to dealing with the high-volume business-critical workflows, such as checking the eligibility of people with insurance claims, that Pega specialises in.
“It would be complete madness to try to replace what Pega does with fleets of agents running around, kind of guessing at what to do, trying to make a fist of it. That would be an absolute disaster,” he added.
But he said there are other, less critical applications where fleets of AI agents would be just fine, such as building marketing campaigns, translating advertising copy, or checking an online product catalogue is up to date.
Software agents, such as Claude Cowork and Microsoft Scout, sit on the desktop and can help people schedule emails and work through tasks relatively easily.
He said more software suppliers are coming around to a similar combination of deterministic workflows and AI agents, and are using their own technologies to execute agentic AI safely. This includes using their own platforms to provide plug-in skills for AI.
Salesforce, for example, has introduced AgentForce agents, and is using workflows and templates. A German supplier, Cumunda, which competes with Pega, is also using a mixture of deterministic workflows, with AI dynamic decision-making.
