[DSC DACH 25] Governance of Agentic AI - Nescho Topalov.pptx

Agentic AI introduces a new generation of intelligent systems capable of autonomous decision-making and execution, unlocking vast opportunities while raising critical questions about risk, compliance, and ethical responsibility. This session explores how organizations can effectively evaluate, monitor, and govern agentic AI across its lifecycle to ensure transparency, accountability, and trust. Attendees will gain insights into practical frameworks and tools for scaling agentic AI responsibly — balancing innovation with control.

1. Governance of Agentic AI Vienna 15.10.2025

2. The DSC Context DSC DACH = cross-industry data & AI minds Developers → Decision-makers 2025: Agentic AI & Responsible Scaling

3. The Difference AI Agents Software entities that perform tasks autonomously based on programmed logic or goals. Focus: Execution — they follow rules or workflows defined by humans. Agentic AI A new generation of AI agents with reasoning, memory, and self-direction. Focus: Autonomy and decision-making — they can plan, adapt, and pursue goals with minimal human input.

4. The Opportunity and the Paradox “By 2028, one-third of GenAI interactions will involve autonomous agents.” — Gartner, 2024 Automation & augmentation potential But: ethical, operational, and reputational risks grow

5. Why Governance Matters Hallucinations Data leakage Model drift & bias Cost explosions “An AI agent without governance is a liability.” — IBM AI Ethics Board, 2025

6. Governance Pillars Lifecycle governance Risk management Regulatory compliance

7. From Chaos to Control Agents Orchestrator Data & Tools Every agent should live inside a governed ecosystem — orchestrated, monitored, and auditable. That’s the foundation for responsible autonomy. Centralized AI lifecycle governance Manage, monitor and govern any AI: model, app, agent or tool; across IBM and 3 rd party like OpenAI, AWS, Azure, GCP, Meta, etc. Proactive AI risk and security management Proactively detect and  mitigate AI risks, evaluate AI assets, and secure AI deployments with Guardium AI security Trustworthy and dynamic compliance Manage AI for safety and transparency with our regulatory library, automation and  industry standards Platform agnostic: Govern any AI Agent, deployed anywhere Ariba

8. Agentic AI Risks and Challenges Risks Misaligned actions Discriminatory actions Over- or under-reliance Unauthorized use Exploit trust mismatch Unexplainable or untraceable actions Lack of transparency Risks Unsupervised autonomy Data bias Redundant actions Attack on AI agent’s external resource Tool choice hallucination Sharing IP/PI/confidential information Challenges Reproducibility Traceability Attack surface expansion Harmful and irreversible consequences Challenges Evaluation Accountability Compliance Mitigation and maintenance Infinite feedback loops Shared model pitfalls New Emerging areas intrinsic to agentic AI Amplified Known areas intensified by agentic AI

9. Key lifecycle governance activities For agentic systems Experimentation tracking Track agentic app variants and compare results to inform which to push to production Agentic system metrics, monitoring and alerts Oversee elements such as hallucination, answer relevance, and system drift in production and development Traceability Help developers debug agentic app by tracing each step of the user interaction and agent processing Cataloging of agentic AI applications Single consolidated view of all in development and use

10. Agent Onboarding Demo

11. Agentic Tool Catalog Demo

12. Agent Evaluation Demo

13. AI Use Case

14. Example: The BI Agent Conversational business insights, grounded in governed data Transparent queries, explainable answers Built on watsonx

15. Example: Orchestrating AI Agents watsonx Orchestrate = create, connect, monitor agents 400+ ready connectors & tools Built-in AgentOps for oversight

16. The Cost of Ignoring Governance ⚠️ Sandbox success → production failure ⚠️ Shadow AI, untracked spend ⚠️ Compliance exposure

17. Our Approach Co-design with governance from day one Pilot safely → scale confidently Govern any model, any cloud Combine people, process, and platform

18. You Are Welcome: 11.11.2025, Vienna

19. Visit our booth to continue the conversation! Connect with me: Nescho Topalov CEO & Co- founder Erdbergstraße 52-60/3/20-21, 1190 Vienna / Austria [email protected] www.topideas.digital

20. watsonx Orchestrate watches Asana for new / updated tasks ; reads title, description , custom fields , attachments , and due dates . Pulls the relevant Bynder policies and brand assets ( filtered by market , product , channel , language ). Runs hybrid checks : hard rules for must / forbidden items + watsonx.ai RAG on retrieved policy passages . Drafts targeted questions to the submitter ; posts in Asana auto- rechecks on reply .

21. Updates Asana fields : Compliance Status, Risk Score, Policy Version, Last AI Check ; attaches a Compliance Report . On approval , syncs Bynder metadata and stores a full audit trail . Security & governance : SSO/OAuth, least-privilege scopes , data minimization , watsonx.governance lineage / monitoring . Outcome : faster approvals , fewer reworks , consistent global compliance , and an auditable record .

22. Use Case Exploration 20 minutes Explore Challenges Together, let’s identify and review the key challenges impacting your company’s efficiency. We’ll dig deeper to understand the root causes and details. Consider challenges within your area from three perspectives: Think about how the processes and the flow of information sometimes create obstacles. Where do misunderstandings, delays, or breakdowns happen? Refer to the examples we’ve shared.

23. Agentic Value 15 minutes Cluster Challenges How might we address the initiatives with agentic and how will it impact your current organization. What objective do we reach? Leaner, Faster, Newer? What is the autonomy level that we want to give to the system?

24. Use Case Prioritisation 5 minutes Prioritise Prioritize considering Impact & implementation easiness. Impact/Value: How much time will this save? What revenue or cost impact might result? Will this reduce risk or improve quality? Implementation Complexity Are all required data sources accessible? How much customization is needed? What integration challenges might arise?

25. Design the Agent(s) 20 minutes Design the Agent Complete the canvas. It's composed of conceptual thinking about agents & pragmatic technical guidelines.

26. About Us Partner for Practical AI Innovation Based in Vienna, active across the DACH region and beyond Experience across various industries – Health, Telco, Energy, IT

27. They trust us .

28. “By 2028, one-third of interactions with generative AI ( GenAI ) services will use action models and autonomous agents for task completion.” Source: Gartner® Press Release, “Gartner Predicts One-Third of Interactions with GenAI Services Will Use Action Models & Autonomous Agents for Task Completion by 2028” watsonx Orchestrate / © 2024 IBM Corporation 31

29. Models ​ Problem-solving​ Logical thinking​ Pattern matching Assistants Information retrieval​ Prescriptive tasks​ Single-step processes​ Agents Multi-step processes​ Autonomous action-taking​ Self-correcting​ A fundamental shift is underway for AI Accelerate AI agent deployment Pre-built Agent s Get started quickly with pre-built AI agents powered with business logic and seamless integration to the tools that power your business. Build custom designed agents Custom-built Agents Design, deploy, and manage AI agents with ease using pro-code and low-code options. Manage all agents in one place Multi-agent Orchestration Easily deploy and manage any agent for any task within a simple and unified user experience optimized to scale. IBM Data Platform | © 2025 IBM Corporation

30. 33 AI Agents An AI agent is an autonomous system that can use tools and collaborate with other agents to plan and act on tasks. After it acts, the agent reflects on the results of its actions, learning iteratively and refining its approach to better align with its defined objectives.

31. Pre-built agents End User Collaborator Agents Orchestrator Agent Agent & Tool Catalog v wxO chat 3rd party UIs IBM Data Platform | © 2025 IBM Corporation v Biz a pps & Processes 34 Custom-built agents Multi-agent Orchestration

32. AI Agents can augment AI Assistants to ta ckle increasingly c omplex tasks and unlock new value for the Enterprise The tipping point – Generative AI Value to th e Enterprise Assistants with AI Agents Fixed flow AI assistants The actual tipping point – AI Agents Fixed flow AI assistants + Gen AI Rigid Fixed Context More Flexible More Contextual Highly Flexible Fully Contextual watsonx Orchestrate / © 2024 IBM Corporation 35

33. Studio Discover, create and manage gen AI and digital automations that combine decisions, tasks, skills, and workflows. AI assistants AI-assisted experiences that are human trained and designed Orchestrator Agent AI Agent supervises and manages how work is executed across assistants, agents, and skills AI Agents in IBM watsonx Orchestrate Supervising Routing Reasoning Planning Custom skills – build new or discovery existing Prebuilt skills and app catalog Customers Employees Subject matter experts Empower customers and employees through simple, intuitive and guided conversations Boost productivity with AI and automation Accelerate time to value with pre-built capabilities or build your own IBM Data & AI / © 2024 IBM Corporation Skills Intelligent task, decision, document and workflow automations augmented by generative AI AI Agents Autonomous AI-driven execution of expert tasks Gen AI catalog powered by watsonx.ai ™ models Agent catalog Agents for Sales Agents for HR Agents for Procurement Custom built Agents

34. The evolution of Generative AI for intelligent business automation Fixed Flow Act as programmed Autonomous Flow Plan and self-correct 37 AI-assisted automation Traditional task automation Autonomous AI orchestration Reasoning Planning Routing Self-correction AI Assistants RAG Gen AI skills IDP Workflow Design Decision logic Process Mining Process Modeling Accelerates and optimizes the design and building of automations AI provides an enhanced user experience and drives higher task completion Allows AI to perform the work reducing the need for human intervention

35. AI in an enterprise is like Ice Cream …

36. … everybody wants to enjoy the ice cream , but it takes a proper cone to do so Agentic Systems Data Products GenAI AI Governance ML-Ops Data Integration Hybrid Cloud Etc.

37. AI building blocks of the future 40 Challenges Compliance Manage AI to meet upcoming safety and transparency regulations and policies worldwide-a “nutrition label” for AI Risk Proactively detect and mitigate risk, monitoring for fairness, bias, drift, and custom metrics Lifecycle Management Manage, monitor and govern AI models from IBM, open-source communities and other model providers (e.g. Meta, Mistral AI) Assistant and Agent Orchestration & Rollout Integration with the existing infrastructure, Self-Service, Automation-integration, maturity level of AI depending on the use case,…