New AI Agents Take on Management Jobs

A new breed of autonomous artificial intelligence called agents is taking over customer service and operations at major companies, making decisions that, until recently, required human managers.

The shift marks a turning point in enterprise automation as companies move beyond basic chatbots to deploy sophisticated AI agents that can navigate complex business processes independently. Commerce.AI’s latest offering exemplifies this trend, with AI systems increasingly trusted to handle sensitive customer interactions and operational decisions that shape company performance.

Commerce.AI launched auto-AGENTS, an AI system that autonomously handles business interactions across voice, chat and text channels. The system includes specialized AI agents for tasks like data retrieval, sentiment tracking and post-call automation, aiming to boost enterprise efficiency while allowing human agents to focus on high-value work.

“While traditional AI approaches have centered around assistance, the ability for AI agents to reason, decide and take action will amplify results,” Archana Kannan, senior vice president of product for work messaging app Slack, told PYMNTS. “Ultimately, agents are going to transform how every user gets their job done, particularly the mundane, common tasks like automating projects, new hire onboarding, generating content or managing IT incidents.”

AI agents are autonomous software systems that can understand the context and make decisions across multiple business tasks. Unlike basic chatbots, they can handle complex workflows independently, from managing customer support tickets to orchestrating supply chain operations.

AI agents have been stealing the spotlight in tech innovation. OpenAI’s new Swarm framework launched last month allows developers to orchestrate multiple AI agents for complex tasks, enhancing collaborative problem-solving capabilities. Skyfire Systems introduced a payment network in August tailored for AI agents, enabling autonomous financial transactions and signaling a shift toward self-sufficient AI operations. These developments highlight a transition from isolated AI systems to interconnected agents, poised to revolutionize commerce, logistics, and creative industries.

ServiceNow is deploying AI agents to handle IT and customer service issues autonomously. At The Ottawa Hospital, similar technology provides patient information while reducing medical staff paperwork.

Commerce.AI’s Smarter Agent Tracking

Commerce.AI said in a Nov. 14 press release that its new auto-AGENTS system can track customer sentiment and retrieve internal documents in real time. Its features are aimed at highly regulated industries like healthcare and finance. The AI software connects with existing enterprise systems like customer relationship management systems and data lakes, although the privately-held company didn’t disclose pricing or current customers.

“This system doesn’t just assist — it autonomously handles tasks,” Commerce.AI CEO Andy Pandharikar said in the release, adding the technology is designed to let human agents focus on more complex work. The system builds on the company’s auto-MATE platform, which was introduced last year.

Kannan said AI agents will complement — not replace — human connections. By using real-time structured and unstructured data to personalize interactions, AI can reflect a company’s brand voice while automating routine tasks.

“At the same time, employees stay involved for moments that require a personal touch,” Kannan said. “Agentforce agents in Slack are a great example of how this balance works. Agents act as an extension of the team, seamlessly stepping in to handle tasks, while humans remain available to build and nurture relationships. The result is agents that enhance, not detract, from customer trust.”

AI Agents Need New Yardstick

As AI agents reshape how companies work, experts say we need new ways to measure their impact. Karli Kalpala, head of U.K. and Ireland and strategy transformation at Digital Workforce, told PYMNTS that organizations must rethink how they measure AI’s value in business operations.

“The brilliance of AI agents is that they are trained to the unique contexts of each business, and as such, demonstrating ROI has to be understood beyond traditional metrics,” Kalpala said.

Success requires enterprise-grade platforms to ensure business user control and robust security, said Kalpala, who specializes in enterprise automation transformation. Forward-thinking companies use AI to predict customer inquiries and perform real-time fraud detection while balancing automated efficiency and human expertise.

“Leading companies aren’t choosing between automation and human connection,” he said. “They’re redefining how these elements work together, combining their strengths to deliver excellent customer experiences.”

Ankur Sinha, chief technology officer of Remitly, a digital money transfer service that helps immigrants send money to families abroad, said AI agents must prioritize customer trust over operational efficiency as FinTech companies race to deploy the systems.

“Efficiency gains may impress shareholders, but trust is what wins customers,” he said. “The true ROI of agentic AI lies in its ability to deliver consistent, reliable experiences that build loyalty over time.”

The digital payments company, which operates in more than 170 countries, is developing AI systems that recognize cultural nuances and adapt to individual customer preferences. However, Sinha stressed that human oversight remains crucial.

“Automation and personalization don’t have to be at odds,” he said. “At scale, personalization can turn customer interactions from transactional to relational, building loyalty and trust.”

Cognizant Chief Technology Officer of AI Babak Hodjat said business metrics, not technical accuracy, should drive the assessment of autonomous AI systems.

“We should think of AI workflows as leading to business decisions that impact business KPI,” he said, citing trading systems as an example where model accuracy matters less than actual risk-return performance.

Hodjat, who leads Cognizant’s AI lab, stressed the need for safeguards, including human oversight triggers and a “disengage button” to halt autonomous operations if needed. The system should then “fall back to an entirely manual or fully predictable mode of operation.”

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