2025: The year ‘invisible’ AI agents will integrate into enterprise hierarchies – VentureBeat

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In the enterprise of the future, human workers are expected to work closely alongside sophisticated teams of AI agents. 

According to McKinsey, generative AI and other technologies have the potential to automate 60 to 70% of employees’ work. And, already, an estimated one-third of American workers are using AI in the workplace — oftentimes unbeknownst to their employers. 

However, experts predict that 2025 will be the year that these so-called “invisible” AI agents begin to come out of the shadows and take more of an active role in enterprise operations. 

“Agents will likely fit into enterprise workflows much like specialized members of any given team,” said Naveen Rao, VP of AI at Databricks and founder and former CEO of MosaicAI.

Solving what RPA couldn’t

AI agents go beyond question-answer chatbots to assistants that use foundation models to execute more complex tasks previously not considered possible. These natural language-powered agents can handle multiple tasks, and, when empowered to do so by humans, act on them.

“Agents are goal-based and make independent decisions based on context,” explained Ed Challis, head of AI strategy at business automation platform UiPath. “Agents will have varying degrees of autonomy.”

Ultimately, AI agents will be able to perceive (process and interpret data), plan, act (with or without a human in the loop), reflect, learn from feedback and improve over time, said Raj Shukla, CTO of AI SaaS company SymphonyAI.

“At a high level, AI agents are expected to fulfill the long-awaited dream of automation in enterprises that robotic process automation (RPA) was supposed to solve,” he said. As large language models (LLMs) are their “planning and reasoning brain,” they will eventually begin to mimic human-like behavior. “The wow factor of a good AI agent is similar to sitting in a self-driving car and seeing it steer through crowded roads.”

What will AI agents look like?

However, AI agents are still in their formative stages, with use cases still being fleshed out and explored. 

“It’s going to be a broad spectrum of capabilities,” Forrester senior analyst Rowan Curran told VentureBeat. 

The most basic level is what he called “RAG plus,” or a retrieval augmented generation system that does some action after initial retrieval. For instance, detecting a potential maintenance issue in an industrial setting, outlining a maintenance procedure and generating a draft work order request. And then sending that to the end (human) user who makes the final call. 

“We’re already seeing a lot of that these days,” said Curran. “It essentially amounts to an anomaly detection algorithm.” 

In more complex scenarios, agents could retrieve info and take action across multiple systems. For instance, a user might prompt: “I’m a wealth advisor, I need to update all of my high net worth individuals with an issue that occurred — can you help develop personalized emails that give insights on the impact on their specific portfolio?” The AI agent would then access various databases, run analytics, generate customized emails and push them out via an API call to an email marketing system. 

Going further beyond that will be sophisticated, multi-agent ecosystems, said Curran. For example, on a factory floor, a predictive algorithm may trigger a maintenance request that goes to an agent that identifies different options, weighing cost and availability, all while going back and forth with a third-party agent. It could then place an order as it interacts with different independent systems, machine learning (ML) models, API integrations and enterprise middleware. 

“That’s the next generation on the horizon,” said Curran. 

For now, though, agents aren’t likely to be fully autonomous or mostly autonomous, he pointed out. Most use cases will involve human in the loop, whether for training, safety or regulatory reasons. “Autonomous agents are going to be very rare, at least in the short term.”

Challis agreed, emphasizing that “one of the most important things to recognize about any AI implementation is that AI on its own is not enough. We see that all business processes are going to be best solved by a combination of traditional automation, AI agents and humans working in concert to best support a business function.” 

Helping with HR, sales (and other functions)

One example use case for AI agents that nearly every industry can relate to is the process of onboarding new employees, Challis noted. This typically involves many people, including HR, payroll, IT and others. AI agents could streamline and speed up the process as it receives and handles contracts, collects documents and sets up payroll, IT and security approval. 

In another scenario, imagine a sales rep using AI. That agent can collaborate with procurement and supply chain agents to work up pricing and delivery terms for a proposal, explained Andreas Welsch, founder and chief AI strategist at consulting company Intelligence Briefing. 

The procurement agent will then gather information about available finished goods and raw materials, while the supply chain agent will calculate manufacturing and shipping times and report back to the procurement agent, he noted. 

Or, a customer service rep can ask an agent to gather relevant information about a given customer. The agent takes into account the inquiry, history and recent purchases, potentially from different systems and documents. They then create a response and present it to a team member who can review and further edit the draft before sending it along to the customer.

“Agents carry out steps in a workflow based on a goal that the user has provided,” said Welsch. “The agent breaks this goal into subgoals and tasks and then tries to complete them.”

How FactSet put AI agents to work

While agent frameworks are relatively new, some companies have been using what Rao called compound AI systems. For instance, business data and analytics company FactSet runs a finance platform that allows analysts to query large amounts of financial data to make timely investments and financial decisions. 

The company created a compound AI system that allows a user to write requests in natural language. Originally, the company had one monolithic LLM and “packed as much context as it could” into each call with RAG. However, this method hit a quality ceiling with around 59% accuracy and a 16-second latency, Rao explained.  

To address this, FactSet changed its architecture, breaking its system down into a more efficient AI agent that called various smaller models and functions, each customized or fine-tuned to accomplish a specific, narrow task. After some iterations, the company was able to significantly improve quality (85% accuracy) while decreasing costs and latency by 62% (down to 10 seconds), Rao reported.

Ultimately, he noted, “true transformation will come from leveraging a company’s data to build a unique capability or business process that gives that business an advantage over its competitors.”