Levi Strauss & Co. has built specialized AI agents across HR, finance, IT, and retail operations and is now constructing a Super Agent to connect them into a single interface, according to a Microsoft customer story published June 4. An employee asking about inventory, submitting an IT request, or initiating an HR process will reach the correct system through one entry point, eliminating the manual routing that currently moves work between siloed enterprise software.

“This isn’t just about a tool, it’s a wholesale workplace transformation,” Jason Gowans, Levi’s chief digital and technology officer, told Microsoft.

The Cross-Function Problem

Enterprise software was built function by function. Finance has its system, HR has its own, IT has another. Work that spans all three moves through each one separately, routed by employees who know which system to open next. That handoff layer is where time and cost accumulate, according to PYMNTS.

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, in the Microsoft case study.

Multi-Agent Workflows Up 300%

The Levi’s deployment reflects a broader pattern. Multi-agent workflows grew more than 300% over several months as organizations moved from pilots into production, according to Databricks data reported by PYMNTS in February. The distinction matters: single AI assistants respond to prompts, while multi-agent systems manage workflows by passing tasks between specialized agents under defined rules.

Goldman Sachs is applying similar logic in financial services, testing AI agents built with Anthropic’s Claude to automate transaction reconciliation, trade accounting, and client onboarding, according to PYMNTS.

CFOs See the Budget Case

PYMNTS Intelligence research 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 (what most companies do today) and acting (agents initiating adjustments within guardrails automatically).

Ramp launched Applied AI Solutions in June specifically for workflows spanning multiple systems where exceptions require autonomous judgment within policy bounds, according to PYMNTS. “In finance, every decision depends on buried layers of context: the policy, the vendor, the contract, the approval chain, and the exception history,” said Ori Daniel, head of AI solutions at Ramp.

Orchestration Over Replacement

The constraint keeping work fragmented across enterprise systems has never been technology. The systems were never designed to talk to each other. Super agents sit on top of existing infrastructure and coordinate across it rather than replace it. For enterprises that spent decades building function-specific software, that distinction is the difference between a multi-year migration and a deployment that layers onto what already exists.