Fortune 500 enterprises will be deploying armies of AI agents by 2028 — to the tune of 150,000 digital “workers,” Gartner said in a survey released this week. That would represent a sharp jump from the average of about 15 agents deployed per company last year.

And agents as actual co-working tools are likely to go mainstream within the same time frame, said Max Goss, senior director analyst for Gartner. These agents won’t just be text boxes from which users get responses, but assistants to which actual work can be delegated.

“We’ve seen a sort of new appreciation in the industry of what agent AI can do,” Goss said.

Many AI agents can already handle basic tasks such as summarizing documents on behalf of workers. Upcoming agents will be able to take spreadsheets and word documents, automate work, and offer an interface that makes the tools friendlier to use, Goss said.

That’s already happening in applications such as Google Workspace and Microsoft 365, with easy-to-use AI interfaces, automated workflows and collaboration. 

Despite the fast uptake for agentic AI, fully autonomous agents are uniquely to be in place in just two years, Goss said. Humans will still need to be part of the loop from a security and governance standpoint, with semi-autonomous agents trusted to handle multi-step processes in specific domains replacing prompts.

The 150,000-agents-per-organization estimate is a ballpark figure pulled from multiple surveys and data gathered by Gartner. “We’ve got some good numbers now on agent usage and we can see how it’s been growing,” Goss said.

Gartner’s aggressive projection, if it holds true, represents a more optimistic view of the technology than other surveys — some of which have pegged failure rates for generative AI tools as high as 95%. But companies like EY and Lumen have demonstrated successful agent deployments, mostly for knowledge workers and customer service.

“Agentic usage tends to be…most valuable in the customer service and data and analytics space…. Those are areas where we have more confidence that AI tools can add value,” Goss said.

Agentic AI use in other areas is likely to advance more slowly. For example, highly regulated fields such as finance and healthcare have to be careful with agent deployment and require guardrails in place to reduce hallucinations and errors.

And agents at the scale envisioned by Gartner will need 100% uptime, just like servers. As a result, companies will likely have to ensure agent reliability by spreading them across multiple models and hardware resources, Goss said.

Excessively high use has at times prompted companies like Anthropic and OpenAI to  shut down access to the large language models (LLMs), undermining the reliability of AI services within enterprises.

There are many things IT leaders can do now to prepare for successful deployments, Goss said, such as sanctioning agent use and pro-actively allowing them to be deployed. “If they just block all agents, then employees…are going to probably go around your controls…. They might use unsanctioned tools otherwise known as shadow AI and I think that’s a greater risk,” he said.

Decision-makers will need to guard against AI agent sprawl, and put the right controls in place to govern them. “If you don’t have any visibility of them, then that’s a huge risk for the organization,” Goss said.

Poor management can also leave gaps that break processes or create security vulnerabilities.

And as AI automates legacy business processes, new processes will need to be drawn up for agents. “I don’t think it’s a good idea to be like, ‘Well, this is the process we’ve already done and let’s slap an agent on top of it and see what happens’…. Process design and agentic AI go hand in hand,” Goss said.

genetic AIHe argued that companies should be prepared for some agentic AI tools to fail, which can happen even with safeguards in place to minimize risks. “That [failure] is kind of okay, because actually we need…to understand where these tools can help us and where they can’t,” Goss said.