Presented by SAP
At VB Transform 2026, Max McPhee, senior solution advisor at SAP, spoke with Rob Stretchay, lead analyst at VentureBeat Research, about what it takes for enterprises to move beyond chatbots to autonomous AI agents that can execute real business processes. He argued that the difference comes down to grounding those agents in a company’s own context rather than general knowledge.
“Where we’re starting to see more emergent behavior of it feeling like a coworker rather than an assistant, is where we’re able to provide context on the actual enterprise rather than being able to use more of the standard knowledge,” McPhee said.
That’s the gap that still separates most enterprise chat software from genuinely agentic systems.
Building enterprise context with knowledge graphs
The same principles companies use to onboard new employees also apply to agents, adapted for software that retrieves information differently than humans do.
“When you are onboarding a new agent, I think it’s important to acknowledge how you might onboard a new employee, but tune that for an agent,” McPhee said. “The way that is really powerful is using knowledge graphs and having vector-embedded data, because that’s a really easy format for an agent to be able to find and retrieve information.”
That same grounding is also what keeps an agent from stumbling over an enterprise’s internal shorthand, a problem that’s acute in SAP’s world.
“Being able to provide that tribal knowledge in the format that’s easy for it to consume helps to provide a really nice result with your agents versus a chatbot that might say, ‘Well, what does that acronym mean?'” he said.
Bringing governance, identity, and security to autonomous agents
Governance is an area where SAP’s history works in its favor, and the controls have been evolving for systems that act with more flexibility than earlier automation did.
“That’s where SAP really has a good home, around that governance and process control,” McPhee said. We’re a 50-year-old process company, modernizing that governance to be able to handle the flexibility that comes with agents running.”
One consequence is a renewed role for machine learning in validating agent behavior.
“It’s becoming a bit of a revival of machine learning,” he added, pointing to customers that run agents within a process but then layer in anomaly detection and machine-learning-based validation as a guardrail. This is the same approach SAP had long used for intelligent approval recommendations.
Identity and permissions carry that governance into execution. Under this model, both the human and SAP’s Joule, the generative AI assistant embedded across the company’s cloud applications and Business Technology Platform, must hold the rights to access a given system. Even if a user has permission to access S/4, they cannot do so through Joule unless the assistant has also been provisioned for that access, closing off the risk of using an agent to route around access controls.
Balancing standard SAP with customized enterprise landscapes
Much of McPhee’s work involves reconciling SAP’s own knowledge with decades of customer customization and non-SAP systems. As he put it, many customers tell SAP, “You’re only 10% of my landscape,” a reality that has shaped the company’s recent strategy.
Recent acquisitions such as LeanIX, which McPhee likened to “Google Maps for your architecture,” and process-mining company Signavio are intended to help map that non-SAP majority so SAP’s agents can understand how enterprise systems interconnect. The company has also invested in Berlin-based automation company n8n and is embedding it natively into Joule Studio, its intent-based, low-code environment for building agents.
McPhee warned that companies also need to modernize older on-premises systems or risk running into limitations as they expand the use of autonomous agents.
“You’re going to probably run into throughput issues, and you’re kind of trying to drive a Ferrari around a dirt track,” he said. “You’ve got to upgrade the track first if you want to drive a Ferrari.”
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