The Agent Governance Problem: What 34,000 SAP Customers Are Learning
The Number That Masks the Real Story
Brenda Bown, CMO of SAP Business AI Marketing, shared adoption data at TechEd: "34,000 customers are using AI" capabilities from SAP.
That's significant growth less than two years after Joule's launch. But the number doesn't reveal what matters most: what happens when you have dozens of agents running across your enterprise?
SAP introduced 22 new Joule Agents at TechEd, bringing the total to over 40 across finance, HR, supply chain, and other functions. Add partner-built agents (Bharat Sandhu said "40 by end of year") and custom agents companies are building themselves, and the number multiplies quickly.
This creates a governance problem the industry hasn't solved: how do you manage, monitor, and optimize autonomous AI agents at scale?
SAP's Solution: The AI Agent Hub
The SAP LeanIX AI Agent Hub is now generally available. It's the first enterprise-grade attempt to solve agent governance across an organization.
The hub provides a centralized cockpit to discover, govern, manage, and optimize all AI agents—whether built by SAP, third parties, or custom-developed internally.
Technical capabilities include:
Agent Discovery and Inventory: Visibility into every agent running across the enterprise. Integrations with Google Cloud and Microsoft (planned Q4 2025) will extend visibility beyond SAP systems.
Business Capability Mapping: IT managers can map agents to business capabilities, uncover deployment opportunities across functions, and align each agent to enterprise outcomes.
Lifecycle Management: Organizations can manage agents in the context of their enterprise architecture, tracking deployment status, performance, and business impact.
MCP Server Management: Capabilities to discover and manage MCP servers (expected Q1 2026) provide visibility into the tools and data sources agents can access.
The hub addresses a reality most enterprises are just beginning to face: agents are easier to deploy than to govern.
Where Agents Are Actually Being Used
When asked which industries or use cases are seeing the fastest AI adoption, Brenda Bown identified four areas:
HR
"People Intelligence Agents help HR teams address organizational challenges by spotting trends in people data and recommending actions for retention, compensation, and skills distribution."
The Career and Talent Development Agent transforms succession planning by prompting managers for updates, recommending successors, and generating personalized development plans. The Payroll Agent helps employees understand their pay and helps administrators strengthen payroll accuracy.
Service and Support
"Digital Service Agents increase customer contact center efficiency by providing accurate responses based on customer context and knowledge bases."
The HR Service Agent was specifically highlighted as saving "time and cost spent on HR policy inquiries by reasoning over employee questions and providing immediate, accurate answers."
Supply Chain and Manufacturing
"Production Planning and Operations Agents automate production order release by checking material, capacity, and scheduling availability."
The Supplier Onboarding Agent accelerates supplier onboarding by orchestrating invitations, monitoring supplier progress, and handling escalations.
Finance
"Cash Management Agents automate reconciliation tasks and identify potential cash shortages and surpluses."
The International Trade Classification Agent helps ensure regulatory compliance by classifying goods for shipping and proposing customs tariff numbers.
The pattern: agents are being deployed first for operations that combine high volume, repetitive decision-making, and access to structured data.
The Trust Progression
Muhammad Alam emphasized throughout the keynote that autonomous operation requires establishing trust first: "After you earn trust and accuracy, and efficiency, you let the agents run autonomously."
The practical implementation path has three stages:
Stage 1: Assistant Mode
AI suggests actions. Humans review and approve before anything happens. This is where most of the 34,000 customers are today.
Brenda Bown confirmed: "Work with assistants and agents. Always be in the loop. Less prone to errors."
Stage 2: Validation Mode
AI takes actions automatically. Humans review afterwards and can intervene if needed. Some customers are beginning to move into this mode for low-risk operations.
Stage 3: Autonomous Mode
AI operates independently within defined guardrails. Only exceptions get escalated to humans. This is still rare in production environments.
Alam was clear about the progression: "This is the job of Joule. After you earn trust and accuracy, and efficiency, you let the agents run autonomously."
What's Surprising About Agent Use
When asked what's surprising about how customers are using Joule Agents, Bown pointed to two things:
Extensibility
"Extending the agents we ship. Building their own agent."
Customers aren't just using SAP's pre-built agents as-is. They're extending them for company-specific processes. A procurement agent might be extended to check company-specific supplier approval workflows. An HR agent might be modified to incorporate organization-specific policies.
Joule Studio (general availability December 2025) is specifically designed to support this. Developers can extend SAP-delivered Joule Agents and build new agents grounded in SAP business data and context.
Agent Interoperability
"The pace of development has been a pleasant surprise, as well as agent interoperability."
Agents working together across departments represents early movement toward what SAP calls the "agentic enterprise."
The BMW example came up repeatedly. Muhammad Alam mentioned it in the keynote: "BMW Agentic Enterprise. Five BMW Groups already creating agents. Working with agents across departments."
The Agent2Agent Protocol
SAP is contributing to the Agent2Agent (A2A) interoperability protocol, which establishes a foundation for AI agents to securely collaborate across platforms.
The first implementation is expected to be released in Q4 2025.
This matters because agents built by different teams, on different platforms, using different frameworks need to interact. A supply chain agent from SAP needs to coordinate with an inventory agent built by a partner. An HR agent needs to pass information to a finance agent for budget planning.
The A2A protocol provides standardized communication so agents can work together without custom integration for every interaction.
The 22 New Joule Agents
SAP introduced agents across seven business areas at TechEd:
Business Transformation Management (5 agents):
- Screen Guide Agent: Provides live on-screen guidance navigating SAP Signavio
- Value Case Creation Agent: Converts process mining insights into structured business cases
- Dashboard Analyzer Agent: Interprets process mining dashboards to identify trends
- Process Content Recommender Agent: Recommends relevant process content from best practices
- Workspace Administration Agent: Enables faster user onboarding with correct access levels
Finance (2 agents):
- International Trade Classification Agent: Classifies goods for shipping with customs codes
- Cash Management Agent: Automates bank statement reconciliation
Spend (2 agents):
- Bid Analysis Agent: Generates comprehensive bid analysis summaries
- Receipt Analysis Agent: Fills gaps in expense entries and verifies vendor details
Supply Chain (3 agents):
- Production Planning and Operations Agent: Automates production order release
- Change Record Management Agent: Recommends next steps for engineering changes
- Supplier Onboarding Agent: Orchestrates supplier onboarding and monitors progress
Human Capital Management (4 agents):
- HR Service Agent: Provides immediate answers to HR policy inquiries
- People Intelligence Agent: Spots trends in people data and recommends actions
- Career and Talent Development Agent: Generates personalized development plans
- Payroll Agent: Helps employees understand pay and detects payroll issues
Customer Experience (1 agent):
- Digital Service Agent: Provides accurate customer service responses with context
Cloud ERP and Industries (1 agent):
- Utilities Customer Self-Service Agent: Delivers tailored answers for utility customers
General availability for all agents is expected Q1 2026.
When asked how customers decide which agents to deploy first, Bown said it "depends on the business function" but emphasized solving "complex business process - HR, supply chain."
The ROI Question
When asked about average ROI from implementing agents, Bown gave the honest answer: "It depends on the customer."
While not satisfying, it reflects reality. ROI varies based on:
- How many people the agent affects
- What manual processes it eliminates
- How much time it saves per interaction
- Whether it enables new capabilities or just accelerates existing ones
SAP has updated its ROI estimator for Business AI to help customers model returns. The tool calculates projected ROI based on specific AI features and Joule Agents customers plan to use, explains assumptions, and lets customers request customized quotes.
The tool is generally available now.
But the data from Muhammad Alam's fireside chat suggests significant potential: "7x to 12x value with AI in sprints as learning increases."
That's productivity improvement, not cost savings. Alam framed it as acceleration: "40,000 workers with enough backlog for 200,000. AI is going to be about acceleration."
The Complexity Perception
Bown noted that the biggest misconception customers have about implementing SAP's AI tools is "complexity" and "perception."
This is partly self-inflicted by SAP and other enterprise software vendors. When you announce 22 new agents, multiple deployment paths, evolving architectures, and dozens of partner integrations at a single event, you create genuine confusion.
Bharat Sandhu acknowledged this: "While it's complicated to learn, gen AI will help with learning, adoption, and ROI."
The irony: generative AI is supposed to simplify learning SAP's platform. But that assumes customers have already deployed the AI tools to help them learn. It's a chicken-and-egg problem.
SAP's practical response: free training through the Coursera partnership. SAP committed to equipping 12 million people worldwide with AI skills by 2030.
Brenda Bown explained the rationale: "Equip customers, and the population in general, with AI skills. Upskill the workforce of the future."
The commitment builds on SAP's success training over 4 million people since 2022.
What's Available Now vs. What's Coming
This timeline matters for organizations planning agent deployments:
Available Now:
- 40+ Joule Agents across business functions (most in production)
- SAP LeanIX AI Agent Hub for governance
- Agent builder in Joule Studio (general availability December 2025)
- Pro-code agent development on SAP BTP
Q4 2025:
- Agent2Agent protocol implementation
- Integrations with Google Cloud and Microsoft for broader agent discovery
- Additional Joule Agents reaching general availability
Q1 2026:
- 22 new Joule Agents announced at TechEd
- System-triggered agents in Joule Studio
- Extensibility of SAP-delivered Joule Agents
- MCP server management in AI Agent Hub
- Centralized agent monitoring
H1 2026:
- Additional governance and monitoring capabilities
- Expanded partner-built agent ecosystem
When asked what customers can do if they want to start building agents this week, Bharat Sandhu said: "Go to build. Everything is generally available in a couple of weeks."
The Differentiation Question
SAP faces competition from Microsoft (Copilot), Salesforce (Einstein), Oracle, and others. When customers ask "why SAP AI vs. building our own with ChatGPT," Bown's answer focuses on three factors:
Trust: SAP provides governed, secure AI that meets enterprise compliance requirements.
Governance: Centralized management through the AI Agent Hub provides visibility and control that's difficult to achieve with custom-built agents.
Data Breadth and Depth: "Breadth and depth of the data" that comes from being embedded in business applications. Agents built on SAP have immediate access to business context that would take significant effort to replicate elsewhere.
The implicit argument: building agents on generic LLMs might be faster initially, but enterprises eventually need governance, security, and business context. SAP provides that foundation.
What Governance Actually Looks Like
For organizations deploying agents now, SAP's governance approach has several components:
Inventory Management: Track every agent (SAP, partner, custom) in one place through the AI Agent Hub.
Business Alignment: Map agents to business capabilities so you understand which processes are augmented by AI.
Performance Monitoring: Track agent behavior, success rates, and business impact.
Access Control: Define what data and systems each agent can access through MCP gateways and integration governance.
Lifecycle Management: Manage agent versions, updates, and retirement in the context of enterprise architecture.
The governance tooling is maturing, but it's available now. Organizations don't need to wait for perfect governance to start deploying agents.
What Comes Next
The 34,000 customer adoption number indicates SAP's AI capabilities have moved beyond pilot projects for a meaningful portion of the customer base.
The next phase—moving from assisted to autonomous operation, scaling from a few agents to dozens, proving measurable business value—is where the industry will learn whether agentic AI delivers on its promise.
SAP's governance approach through the AI Agent Hub provides infrastructure to support that scale. Whether it's sufficient will become clear as more customers move from testing to production deployments over the next six months.
For developers and architects, the practical question is: how many agents can you realistically deploy and manage before governance complexity overwhelms the productivity gains?
The answer will vary by organization, but the fact that SAP built governance infrastructure before fully scaling agent deployments suggests they're taking the question seriously.