Super Agents Are Connecting What Enterprise Software Kept Separate

Enterprise software is built around functions. Finance has its system. HR has its own. IT has another. Work that touches all three moves through each one separately, handed off by someone who knows which system to open next. That handoff is where time and cost accumulate, and it is where artificial intelligence (AI) super agents are aimed.

Levi Strauss & Co. has built specialized AI agents across HR, finance, IT and retail operations. It’s now building a Super Agent that will connect them into a single interface, the company detailed in a Microsoft customer story published June 4. An employee asking about inventory, submitting an IT request or initiating an HR process will reach the right system through one entry point without having to navigate each separately.

“As a best-in-class direct-to-consumer retailer, the biggest thing that’s changed for us is the speed at which we need to operate,” Jason Gowans, Levi’s chief digital and technology officer, said. “This isn’t just about a tool—it’s a wholesale workplace transformation.”

Levi’s built the specialized agents first, deploying AI tools across finance, design and HR before adding the orchestration layer on top. “Human/agent collaboration at Levi, I believe, is going to be all about augmentation—giving time back,” said Sheena Kunhiraman, Levi’s vice president of HR technology and analytics.

Multi-Agent Workflows Grow 300% as Enterprises Move From Pilots to Production

Levi’s approach reflects a broader shift. Multi-agent workflows grew more than 300% over several months as organizations moved from pilots into production, according to Databricks data that PYMNTS reported in February. Single AI assistants respond to prompts. Multi-agent systems manage workflows, passing tasks between specialized agents under defined rules. One produces an answer. The other produces an outcome.

Goldman Sachs is applying the same logic in financial services. The bank is testing AI agents, built with Anthropic’s Claude, to automate transaction reconciliation, trade accounting, client vetting and onboarding, work that has resisted automation for decades because it requires processing large volumes of data against strict regulatory requirements, PYMNTS reported in February.

43% of CFOs Say Agentic AI Could Reshape Budget Planning

The business case runs through finance as much as operations. PYMNTS Intelligence found that 43% of CFOs said agentic AI could have a high impact on dynamic budget planning, and nearly half already use AI to monitor working capital and cash flows. The gap is between monitoring and acting. Agent networks can update projections, flag variances and initiate adjustments within defined guardrails, without routing each step through a human.

Ramp launched Applied AI Solutions in June for workflows that span multiple systems and require judgment when exceptions occur. “In finance, every decision depends on buried layers of context: the policy, the vendor, the contract, the approval chain, and the exception history,” Ori Daniel, head of AI solutions at Ramp, said in a news release. The tool captures that context and turns it into agents that complete work within controls finance teams define.

The constraint keeping work fragmented across enterprise systems is not technology. It is that the systems were never designed to talk to each other. Super agents sit on top of that existing infrastructure and coordinate across it rather than replace it.

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