Companies deploy AI agents for productivity gains, but face workforce fears and accountability gaps

Was 2025 Really the Year of AI Agents?

On 5 January 2025, OpenAI CEO Sam Altman outlined his vision for 2025 in a post on his personal blog. In it, Altman proclaimed that "in 2025, we may see the first AI agents 'join the workforce' and materially change the output of companies." His remarks set the tenor for the AI industry through 2025. But did AI agents actually join the workforce in 2025? The answer is yes, absolutely -- or no, not at all. It depends on who you ask. Michael Hannecke, a sovereign AI and security consultant at Bluetuple.ai, says that "everyone" is looking into how to use AI agents. "But there's also a kind of disillusionment. It's not that easy. You don't just throw AI at anything, and it just works." While many industries have expressed interest in AI agents, programmers and software engineers have seemingly leapt towards the front of the pack. Brandon Clark, senior director of product and engineering at Digital Trends Media Group, shares this enthusiasm. He has fully moved his work into AI tools and now trusts the capabilities of AI agents in most situations. "I use Cursor as my daily driver to develop code," says Clark. He also frequently uses Anthropic's Claude Code, bouncing between Cursor and Claude, not only because he has some preference for the tasks each are best at solving, but also to get around usage caps -- which gives an indication of just how frequently he uses agents. "Sometimes I run out of tokens on Claude Code [...] at that point I'll switch back over to Cursor and just continue my work." As with many programmers, Clark's willingness to use agents is in part because of his background. Clark has years of experience working with integrated development environment (IDE) software. An AI-infused IDE, such as Cursor, presents agentic AI in a way that plugs into existing tools and workflows with relative ease. His quick adoption also shows the ways AI agents are well equipped to handle some software engineering tasks. For example, tests are code used to verify that software is operating properly by testing its function against inputs and outputs known to be correct. Tests are important but repetitive and don't often require novel thinking to implement, which makes them easier for AI agents to handle. "It's at the point where I don't even need to be involved. As part of the [AI] system instructions, I say that any time it writes a new feature, make sure to also write tests for it. And while you're at it, run the tests, and if anything breaks, fix it," Clark says. Programmers have also felt empowered by the invention of new ways to integrate AI across software, such as Anthropic's Model Context Protocol (MCP) servers (introduced in November 2024) and Google's Agent2Agent protocol (introduced in April 2025). These allow agents to call on software to complete or verify their work. For example, Cursor has browser tools that can be called as an MCP server. An agent programming for the web can use it to check the results of its work. For Clark, 2025 truly was the year of AI agents. He was able to experiment with them early in the year, and the results he saw only improved as better models were released and AI-focused coding tools improved. Others have had a more mixed experience. Hannecke, an AI consultant based in Germany, saw no shortage of interest in AI agents through 2025. Yet when it came time to think more seriously about deployment, organizations often ran into trouble. "I have only seen three or four use cases where companies have [AI agents] in production," says Hannecke. "Most others are still in a development phase, still evaluating, still testing, due to the insecurities that can come with it." He says many organizations react with a degree of "German angst" over the risks that come with AI automation. "There are a lot of things we're not quite 100 percent sure about with AI agents." Some people believe that agents are productivity boosters with little to no downside; others see them as promising but early technology; still others see them as fundamentally dangerous. German and European regulations contribute to this reaction, to be sure, but they're not the only reason for caution. Jason Bejot, senior manager of experience design at Autodesk, which makes 3D design software, articulated a concern that will be relatable to engineers across many fields: accountability. "That's one of the big challenges. [...] How do I actually get it to work, to make it precise, so that I can get it built?" Bejot asks. Autodesk has an agentic AI tool, Assistant, that can field questions from users of Autodesk software including AutoCAD, Autodesk Fusion, and Revit. However, as it exists today, the assistant is largely designed to be only that -- an assistant. It can summarize information and provide guidance, but it's not meant to take the reins and engineer a solution autonomously. "You need to be able to have a clear through-line. If architect A has updated their sketches using the assistant, that person is still accountable for those updates," says Bejot. "So, how do you create that level of accountability across the board? It's something we're very conscious of." The varying experiences of Clark, Bejot, and Hannecke underscore the wide range of outcomes from AI agents through 2025 and into 2026. For some, agents are already working as Altman speculated. For others, there's a lot more work to be done before agents can deliver. Kiana Jafari, a postdoctoral researcher at Stanford University, has studied this gap. She co-authored a paper that found technical metrics like accuracy and task completion dominate 83 percent of AI agent assessments. These are metrics that can be verified and systematized, reflecting Clark's experience as a programmer. However, technical accuracy isn't the only metric worth attention. "Most of the agentic systems that we are working with right now are in theory doing very well in terms of accuracy," says Jafari. "But when it comes down to people using it, there are a lot of hurdles." In fields where professionals bear personal responsibility for outcomes, even AI agents that achieve a high standard of technical accuracy may not perform well enough. Jafari's interviews with medical professionals have made it clear why this is the case. "What they all say is, 'If there is a 0.001 percent chance that this could make mistakes, that is still my name. That is on me if it's wrong.'" This can result in AI agents backsliding from an active to advisory role. This can help explain the extreme divergence in the reception of AI agents. Some people believe that agents are productivity boosters with little to no downside; others see them as promising but early technology; still others see them as fundamentally dangerous. The reality is that AI agents can be all of these things, depending on the task they're set to solve. "There's still the need for the human in the loop," says Hannecke. "2025 was a lot of 'let's play with it, let's prototype it.' 2026 will be the year we put it into production, and find out what will be the difficulties we have to deal with when we scale it."

How CEOs bringing AI agents to work are preparing customers and employees

But CEOs who have overseen successful AI agent deployments say it's about treating the tools as an additive to existing jobs, not replacements. Companies are investing heavily in AI-powered agents, pointing towards a future where customers' first lines of communication when seeking an answer or wanting to buy something may be a chatbot. In October, Walmart announced a deal with OpenAI that will enable shoppers to both find and buy items without leaving ChatGPT. The retailer also now has an AI agent in its app that can answer and recommend products. On Walmart's earnings call in November, CEO Doug McMillon said agentic AI will be one of the growth drivers for the retailer's e-commerce business. He said the technology will "help people save time and have more fun shopping." In January, Walmart said customers will soon be able to use Google's artificial intelligence assistant Gemini to more easily discover and buy products from the retail giant and its warehouse club, Sam's Club. Those investments in agentic software are also being tasked with helping workers send emails, summarize notes and increase their overall productivity. All of that puts added pressure on companies to ensure this approach actually works for all stakeholders. At the recent annual customer conference of telecommunications software and services provider Calix, CEO Michael Weening asked the room of broadband service provider executives if any of them didn't have enough to do. "And we raised the lights, and no one raised their hand. I asked if any of them were sitting around lazily, waiting for their jobs to be displaced because they didn't have anything to do. No hands," Weening said. "The message I hear from everyone all the time is 'I have way too much to do,' so my message was how do you free up time to do more and how do you add capacity so you can grow." In October, Calix rolled out AI agents across the platforms that its broadband service provider customers use. That includes agents to help marketers generate subscriber offers, customer service representatives improve their troubleshooting, have subscriber questions and interactions directed to the right people, and help field technicians automate diagnostics and optimize installations, among other things. From Weening's perspective, this should be welcomed help, but he acknowledges that the messaging around agentic AI from bigger technology company executives that it will lead to layoffs has many scared. Artificial intelligence was cited as the reason for more than 55,000 layoffs across the U.S. in 2025, according to data from Challenger, Gray & Christmas. That included job cuts at major employers like Amazon, Microsoft and Salesforce. That is being further amplified by tech and AI sector executives touting the potential for AI to wipe out jobs across several industries. Earlier this week, Anthropic CEO Dario Amodei wrote in an essay that AI will have a broader shock to the labor market than other technological advances and could wipe out jobs across several industries. "The technology is not replacing a single job but acting as a 'general labor substitute for humans," Amodei wrote. All of that is leading to declining worker sentiment towards AI. A January 2026 poll by Mercer found that 40% of employees are concerned about job loss due to AI, compared to 28% in 2024. Weening said that the "demonization and freaking out" that is stemming from executive messaging around AI a real concern and will distract from the technology's potential. "Agentic AI is purely a workflow, and every task in a workflow is an agent," he said. Instead, he said companies need to make an effort to showcase how AI agents are "your new teammates to help you do a better job." There are ways to try to soften the introduction of AI agents. When Calix rolled out the technology across its platform, it took the step to transform the agents into what Weening called, "really non-aggressive, very friendly, Teletubby-like characters." "My view is they're becoming part of your workforce. You think of them as part of your team," he said. In fact, some companies have begin counting AI agents within their overall workforce numbers. Consulting firm McKinsey now has 25,000 personalized AI agents and 40,000 human employees, according to data shared by the firm's global managing partner at a recent live taping of the "All-In" podcast at the CES trade show in Las Vegas. That's a similar message to what Weening shared internally, where Calix was an early adopter of Microsoft's Copilot AI companion. "My thought is if we used Copilot ubiquitously, the benefit is we've got data protection, but more importantly, we can signal to the entire employee population we are serious about innovating," he said. So far, the company has had more than 700 employee-generated agents built, Weening said. Calix also identified 40 workflows that the company believed would have a significant impact on productivity if improved by AI. The company's IT team then formalized those tools and rolled them out across the organization. "Are all those change-the-world agents? No, it may be something as simple as a tool to write an email faster, but at least they're playing with it," Weening said. Weening said in all his communications around AI, he has been clear about the balance that needs to be struck around risk and speed, especially when it comes to critical data. "I see all the spectrums from those that are very concerned and the others who are so focused on moving fast they're oblivious to the risk," he said. "I think many people are struggling with finding the right way to this, and I think that comes back to having very clear guidelines with regard to protecting data, whether that's your customer data or how your partners are managing our data." Weening said he acknowledges that jobs will be impacted by these new agentic AI tools. "I heard a saying the other day and now I repeat it all the time that 80% of jobs will change 20%, 20% of jobs will change 80%," he said. That means his message internally is that these tools will allow workers to take on new tasks as Calix continues the growth trajectory that it is on. While headcount growth might not continue to double because of AI productivity gains, it will still grow exponentially. "We're in this disillusionment phase with AI right now where everyone is asking, 'Where's the ROI?'" he said. "What we have to build is a mindset of change inside the company to embrace AI and look at it pragmatically so that we can evolve where we are." "We're making great progress in that regard, but it's going to accelerate at an insanely fast pace in 2026," he added. From Everest Group CEO Jimit Arora's vantage point, there have been several enterprise-level systems that have helped to transform the way that business is done, from systems of record like ERPs, systems of engagement like CRMs, and systems of insight where insights and data start to be put into action. AI agents, as he put it, are part of the new "systems of execution" category. "When you use a combination of deterministic machine learning, AI, generative AI and agentic AI as currently defined, that's when value happens," Arora said. While experimentation is well underway to meet that future, Arora said he would still call this moment "pre-agentic." "We still don't have true agency with the agents; we are building agents that can do actions, and there's a difference," he said. "We've reached autonomy in some ways, but we haven't given them agency." However, Arora said that he expects to start to see efforts to do just that happen in 2026, especially in what he said are the "three biggest use cases" for true agentic AI: in the software development lifecycle; service desk applications within the HR, IT and finance functions; and customer experience. But as those efforts move along, Arora said companies should make sure they avoid what he calls "PTSD," or "process debt, tech debt, skills debt and data debt." "If you have the right data, but you're trying to identify a broken process, you're going to amplify the brokenness," he said. "You also need the right skills, because applying yesterday's skills to tomorrow's problems won't work. And through all of this, technology can be the easy part." Still, Arora said that he does caution CEOs and executives that are hoping to see significant agentic AI results next year. "I want to take some inspiration from cloud: AWS came out in 2006, Google Cloud Platform in 2008 and Azure in 2010. It took us a good 15 years to get to 50% public cloud adoption," he said. "That true unlock is going to happen in the next three to five years, but we're gong to see some meaningful progress. We have to think of it as a capex project; that's when you'll get the true unlock, otherwise you'll be stuck in the valley of incrementalism, or pilot purgatory." Bruno Guicardi, the co-founder of information technology company CI&T, said that when it comes to building out your own agentic AI, he favors a structure that "gives autonomy to the agents gradually in systems where there is a level of supervision that you can define when you actually retract the supervision." Guicardi used the example of automated client responses. Where someone would initially review every AI-created response before the response is sent, over time, if the responses were deemed acceptable, that person would start to review less of them and then allow the AI to send them automatically. "We think that this will be a way to build confidence," he said. "It's about building a system that earns control, that earns the trust to be autonomous."