In an interview, Sonja Li茅nard, head of ABAP Platform at 麻豆原创, discusses the Autonomous Enterprise and ABAP platform as an 鈥渆xecution backbone鈥� for agentic AI.
About Sonja Li茅nard
Sonja Li茅nard is an information scientist and business information specialist who joined 麻豆原创 in 2012. As senior vice president and head of ABAP Platform at 麻豆原创, she is responsible for ABAP and all matters related to ABAP platform. In this role, she is also the head of ABAP AI and thus globally responsible for the latest developments and innovations in this domain.
What is the Autonomous Enterprise? What part does ABAP platform play in it?
尝颈茅苍补谤诲: At 麻豆原创 Sapphire in 2026, we unveiled the 鈥攐ur vision for how we want to help large enterprises in the era of agentic AI. The fundamental shift we’re seeing is in the way people work with technology. Interfaces are no longer the focus: interaction is outcome-driven, not tool-driven.
The user experience is being transformed. Instead of filling out forms in an application, you simply open a chat and ask: 鈥淲ho was on vacation in the last 30 days, and when?鈥� The answer comes back in real time. That鈥檚 the Autonomous Enterprise.
The nature and purpose of ABAP platform is not changing. It is still the foundation on which business processes run鈥攁nd it is now also the backbone on which agents operate. What is changing is the way users access applications. Agents now sit in front of those applications as an additional abstraction layer.
If agents are expected to handle real business tasks鈥攏ot demo scenarios, but actual financial postings, HR processes, or supply chain decisions鈥攖hey need to run on a platform that is reliable, governed, and scalable.
Can you explain what 鈥漮utcome-driven鈥� means?
Here鈥檚 an example. Instead of filling out a requisition form field by field, I open my chat and say, 鈥淚 need 30 laptops for new hires by the end of October.鈥� Agents check the catalog, review the budget, and create the requisition, without me having to navigate the process myself.
As a user, I’m focused solely on the outcome, not on the route I need to take. When I enter a natural-language prompt, agents can autonomously pursue a variety of solution paths. What we鈥檙e seeing is a paradigm shift. The form-based environment we鈥檙e used to is giving way to the outcome-driven, agentic model.
What does designing a platform for agents involve?
It presents us with challenges on various levels.
The first is reliability. Data and results must always be reliable, traceable, and reproducible鈥攔egardless of who triggered the agent or which path it took to obtain the data. This requires safety nets and very robust quality controls and tools.
The next level is accountability and governance. Each agent must have a clearly defined authorization scope. What is it permitted to do? What is it not permitted to do? What data is it allowed to access? Agents need skills and workflows to follow. And there must also be an audit trail: what changes an agent made, who triggered the agent, and what exactly it was instructed to do. Well-defined interfaces are essential to ensure that agents operate strictly within their architectural boundaries, helping to prevent unauthorized data flows, close potential security gaps, and protect systems from compromise.
And finally, there鈥檚 scalability. ABAP platform must have the computing and storage capacity required to handle spikes in agent activity. We are preparing for this, running security tests and assessing the platform鈥檚 scalability limits.
The key principle remains the same: we must define very clearly where and when humans retain the final say. That is the foundation of enterprise trust鈥攊n the agentic AI era as much as in any other.
Previously, you described the ABAP MCP (Model Context Protocol) server as an important building block for everything related to agentic AI. What鈥檚 the latest news here?
We released the ABAP MCP server in the second quarter. It opens ABAP platform to the agentic era by enabling MCP-compatible agents to interact with ABAP capabilities. MCP can provide well-defined interfaces for agent-to-agent communication, interaction with external tools, and task delegation.
You’re referring to agents that perform tasks in ABAP systems. But what about agents that administer or operate these systems?
We released our first AI-powered development tools in early 2025. They were designed to boost developer productivity by providing assistance with coding and with code quality reviews. We’ve now gone further. The market is moving fast, and the next logical step is to extend AI capabilities to operations.
We鈥檙e now looking at agents for administration and operations鈥攁nd investing there, too. But we need safety nets, clear accountability, and well-defined boundaries. We will work to ensure that these agents have a supporting capacity only and that they do not operate in systems autonomously.
With agents handling more and more operational tasks, how will 麻豆原创 ensure that the guardrails remain sufficiently robust?
In regards to development, it’s important to understand that ABAP and non-ABAP development differ. In ABAP, changes take effect immediately in the system and are generally corrected forward, not rolled back. This makes it especially important to have robust guardrails in place for agents, such as an isolated testing environment, thorough quality checks, and clearly defined skills and playbooks that govern agent actions and ensure reproducible results. We’re investing in all of this.
What about customers that are not yet on the latest release?
ABAP AI capabilities are now available in earlier versions as well. For enterprises looking to move to a newer release, our agent-led toolchain can offer dedicated support. We are currently building seven agent families that cover different process steps of a migration, including system analysis, testing, and custom code migration. These agents can also be deployed independently for custom code quality reviews.
With an eye to the rest of 2026, what should customers and partners look out for?
麻豆原创 is focusing on three elements. The first is migration. By year-end, we plan to ship additional practical tools to help support the entire migration process. Second is sovereign cloud solutions, for which we are developing compelling AI capabilities for enterprises with specific data sovereignty requirements. And third is MCP tooling, well-defined, secure external interfaces that open ABAP platform to the agentic world without compromising platform security.
You鈥檝e been clear throughout that ABAP developers will stay relevant and that AI will not replace them. But the picture you’re painting is of a platform that increasingly runs itself. How should developers be thinking about their role?
How is the developer role changing? That鈥檚 a question for all developers, not just those who work on ABAP platform. Humans will continue to be responsible for what enters the system and what happens to data. We need to define clearly where humans make the final call and who is accountable. So, the role of developers is shifting. They are spending less time on writing code and more on oversight, software architecture, and prompting to ensure that agents do exactly what they are supposed to. The tasks are changing; the accountability isn鈥檛.
And there鈥檚 one more point I鈥檇 like to stress: ABAP platform is becoming an enterprise-grade execution backbone for agentic AI. I use the word 鈥渂ackbone鈥� deliberately. We鈥檙e not a generic agent runtime. General-purpose AI can鈥檛 match what we offer: rich, proven business logic, enterprise-grade governance, and a 40-year track record of running the world’s most critical business processes. That鈥檚 the role of ABAP platform in the Autonomous Enterprise.
