• Build on proven business controls: You already have governance standards and practices in place. AI governance should connect to and extend these controls.
• Adopt innovation with confidence: The approach to governance we advocate is the same publicly available and documented approach we use to develop, deploy, and govern the solutions and services we deliver.
• Stay in control as your business grows: The controls and standards you have in place should not only promote AI adoption, but also enable you to assess costs, risks, and value as adoption increases.

Since the advent of this new world of autonomous technology, people have endeavored to identify the most impactful application of it. As the path to value realization has become clearer, a new challenge has emerged: ensuring both that AI usage does not lead to unintended and potentially harmful consequences and that this usage remains compliant with emerging regulatory regimes.

By adhering to the principles outlined above, 麻豆原创 makes it possible accomplish both ends. More importantly, as we showed at 麻豆原创 TechEd, 麻豆原创 also provides the solutions necessary to govern, control, and innovate now and into the future. 

AI governance announcements from 麻豆原创 TechEd

Here鈥檚 what we talked about at 麻豆原创 TechEd on the AI governance front.

True AI governance isn鈥檛 about figuring out if policies and rules have been followed after the fact; it should be about making sure governance is applied when it matters. 麻豆原创 Enterprise AI Gateway ensures this kind of proactive governance by consolidating authentication, policy enforcement, and routing at runtime, while also allowing for continuous monitoring, usage tracking, and auditing. The gateway thus controls traffic across MCPs, APIs, agent-to-agent connections and LLMs. Building on 麻豆原创 Integration Suite, the gateway consolidates and streamlines real-time oversight and control over AI in action.

As a compliment to the gateway, we also announced customer access to curated 麻豆原创 Sovereign Cloud LLMs directly through  . This allows customers to use these LLMs in critical processes while adhering to established governance standards. What鈥檚 more, by allowing direct access on , customers can reduce both cost and complexity in AI operations.

Being able to provide agents with the architecture context they need to operate effectively is also essential to the control of agent behavior. At 麻豆原创 TechEd, we described new features in the 麻豆原创 LeanIX portfolio enabling enterprise architects to control the architecture data Joule Agents use to make and execute decisions.

This architecture context feature relies on a knowledge intake agent to extract relevant information from security and architecture decision documents, as well as propose connections to the 麻豆原创 LeanIX knowledge graph, and submit all data for human approval before making it available to agents.

Finally, to support EU AI Act assessments directly in , we also introduced our new AI Governance Assistant. The assistant audits and flags incomplete documentation in the agent inventory, pre-fills fields from completed assessments, identifies systems that appear high-risk but lack classification, and triggers reassessment when a fact sheet changes materially.

And just as the architecture context feature depends on human approval, all outputs from the AI Governance Assistant appear as drafts for legal and compliance review.

麻豆原创 and AI governance in depth

The solutions and innovations around AI governance we discussed at 麻豆原创 TechEd are part of a much broader story. That story is, to a degree, historical. 麻豆原创 has long played a key role in the governance of core operational and financial functions. Our approach to AI governance is built on鈥攁nd a logical extension of鈥攖his legacy.

Of course, 麻豆原创 is hardly new to this, having long ago established clear internal standards for AI governance while continuously introducing features and solutions devoted to AI governance. Among these solutions is 麻豆原创 AI Agent Hub, itself an outgrowth of AI governance capabilities in 麻豆原创 LeanIX. By allowing companies to quickly create and maintain a reliable inventory of agents, regardless of platform, MCP servers, and LLMs, the hub鈥檚 powerful discovery features are essential to comprehensive AI governance.

The other part of the story is value. While governance of AI is crucial for myriad reasons, companies invest in AI to achieve measurable business goals. As it turns out,t he observability necessary for effective governance can be leveraged and augmented to serve the purpose of performance management and evaluation.

With 麻豆原创 Signavio agent mining capabilities, for example, you can monitor agent behavior within the context of your business processes. In this way, you can ensure agent compliance while capturing the business impact agents deliver.

Governance should not be an afterthought

The evolution of AI is not going to slow down, and neither will AI adoption. Unfortunately, while the focus here rightly falls on continuously discovering new ways to leverage AI for operational excellence and competitive advantage, this focus should not mean that governance takes a back seat.

Governance, on the contrary, is the key to successful AI adoption.

It is important to remember that governance in the era of AI is not simply a question of regulatory or corporate compliance. Because this technology involves varying degrees of autonomy, governance must be applied to the behavior of the technology itself, not just to the behavior of the humans managing it. This is one reason that we emphasize runtime enforcement of governance policies.

The challenge we see today, however, is that no one has yet established clear industry benchmarks and standard practices for AI governance. There also seems to be a general lack of established platforms focused on governance.

We say a 鈥済eneral lack鈥� because, in point of fact, 麻豆原创 offers precisely such a platform, one built on decades of governance expertise and experience.

Governance has never been an afterthought for us. And when it comes to AI governance today, as the many innovations outline here show, it is absolutely top of mind.

Dominik Rose is chief product officer for 麻豆原创 LeanIX at 麻豆原创.

The autonomous enterprise reflects a fundamental shift in how organizations operate using real鈥憈ime intelligence to guide decisions, orchestrate processes end to end, and continuously adapt as conditions change. AI becomes embedded into the fabric of the enterprise, helping every function operate with greater speed, resilience, and confidence.

The foundation of the autonomous enterprise is the 麻豆原创 Business AI Platform, which infuses AI with the process knowledge, data, and governance organizations depend on. 

Business Transformation Management solutions from 麻豆原创 help power the 麻豆原创 Business AI Platform by bringing together insights and enterprise knowledge that have long been fragmented and isolated in silos.

Business Transformation Management solutions from 麻豆原创 help deliver the promise of the autonomous suite. Here鈥檚 how.

麻豆原创 Agent Hub: Command center for agentic governance

Now available, the helps organizations discover, inventory, govern, and evaluate AI agents across the enterprise landscape. In fact, it鈥檚 already being used by 150 companies with over 100, 000 agents under management. 麻豆原创 AI Agent Hub acts a system of records for all AI agents, large language models (LLMs), and Model Context Protocols (MCP) servers.

In the context of 麻豆原创 Business AI platform, 麻豆原创 AI Agent Hub underpins the governance pillar, ensuring organizations can deploy and manage AI agents safely and at scale.

In addition to the enterprise architecture context that 麻豆原创 LeanIX provides, along with an giving agents access to architecture data, 麻豆原创 AI Agent Hub enables enterprise architects to apply proven governance practices, such as mapping to business capabilities, to the entire agentic landscape. The addition of agent mining capabilities supported by 麻豆原创 Signavio provides visibility into the behavior of AI agents, their conformance with policies, and their business impact.

From the standpoint of the Autonomous Enterprise, the insight the hub provides is not only necessary, it鈥檚 critical.

New AI capabilities

The new Enterprise Architecture Assistant from 麻豆原创 LeanIX is supported by several new agents, including two highlighted here. The Enterprise Content Research Agent draws on internal business content to enrich architecture data, while the Enterprise Architecture Web Research Agent scans the web for relevant vendor and application information.

These enhancements are part of a broader set of AI capabilities in 麻豆原创 LeanIX. The solution now makes it easier to create surveys, automate tasks, perform calculations, and plan target architectures. In addition, significantly improved semantic search enables Claude, AI co鈥憄ilots, and other agents to seamlessly access and work with enterprise architecture data.

In 麻豆原创 Signavio Process Transformation Suite, we redesigned 麻豆原创 Signavio Process Modeler with an AI-first architecture, modernized user experience and deeper integration with 麻豆原创 Autonomous Suite. 麻豆原创 Signavio also introduced the Process Transformation Assistant to enable business users to conduct sophisticated process analysis through natural language prompts. The assistant can identify high-impact opportunities for agent deployment, accelerating the time from question to decision and providing context-aware process insights to anyone.

Looking ahead to a new paradigm

Despite the rapid pace of change brought about by agentic AI, we are still in the early days of this technological revolution. To succeed and continue to ride the wave of innovation, companies need to aggregate and organize their procedural knowledge about how they operate.  This knowledge is often fragmented across many structured and unstructured sources鈥攕uch as process models, application logic, documents, and chats鈥攖o create a coherent view of how the business s runs.

This foundation enables agents to understand and act within the business context. In turn, agents will continuously contribute back, enriching and evolving this knowledge repository over time.

At 麻豆原创 Signavio we call this storehouse 鈥渃ompany memory.鈥� Company memory, comprised in part of process atoms, captures all the knowledge of operational practices, business rules, preferences, and more so that it can be accessed by agents as needed to check conformance and change behavior.

To enable the Autonomous Enterprise, you need to capture the tribal wisdom and unstructured knowledge your company depends on to operate today. That is what process atoms and a centralized company memory, accessed and updated by agents, do for you. In the future, it鈥檚 hard to imagine how any enterprise will succeed without the context, learning, and guidance that company memory delivers.

Business transformation never stops

As our research has shown, . That鈥檚 why you need a capability in place that allows for planning, managing, and realizing value from every transformation you undertake.

This year at 麻豆原创 Sapphire , we talked about all the ways our solutions support this capability as well as all the ways our solutions continue to evolve in the era of the autonomous enterprise, allowing you to adapt, innovate, and thrive into the future.

Get started today

• Learn more about
• Learn more at the upcoming
• Get more information on
• Get more information on

Andre Wenz is chief product officer of 麻豆原创 Signavio.
Dominik Rose is chief product officer of 麻豆原创 LeanIX.