From AI tool to agent: How the Agentic AI Framework empowers companies to take action

The next generation of AI will be agentic. Companies that still work with isolated tools today risk falling behind tomorrow. The Agentic AI Framework shows how AI can be systematically designed, scaled, and used responsibly—as a practical tool on the path from initial experiments to a true AI organization.

From tool to agent—why structure matters now

In recent years, many companies have introduced AI tools to automate processes or use data more efficiently. But the next step—toward autonomous, agentic systems—requires more than just isolated use cases.

Structures, architectures, and program design are needed to operationalize AI across the enterprise. This is precisely where our Agentic AI Framework comes in: it is not a theoretical model, but a practical orientation system that describes how companies can systematically develop and deploy AI agents. The framework helps to structure complexity, build capabilities in a targeted manner, and design AI programs consistently, rather than getting lost in isolated experiments.

The framework: Orientation in two dimensions

The Agentic AI Framework is based on two central axes that together form a grid:

1. Three architecture layers – Experience, Intelligence and Knowledge: They represent the system level at which AI is designed within the company—from user experience and decision-making logic to knowledge management.
2. Three AI levels – from tool to assistant to agent: These stages describe the development from reactive AI solutions to independently acting, context-sensitive agents.

The combination of these dimensions creates a grid that shows where companies currently stand with their projects and what skills they need to develop in order to reach the next level of maturity.

Dimension 1: The three architecture layers

The layers help companies think holistically about AI systems—from user experience to knowledge base.

1. Experience Layer – Designing the user experience: How do humans and AI interact? This involves touchpoints, dialogue logic, and interfaces—from chatbots to intelligent agents that act independently and proactively.
2. Intelligence Layer – Develop decision-making logic: This level models roles, workflows, and thought processes—in other words, everything that enables agents to plan, decide, and learn independently.
3. Knowledge Layer – Structure and secure knowledge: This is where we define what information the AI uses and how knowledge is organized. Data models, governance mechanisms, and knowledge architectures are created—the foundation for sustainable agent capabilities.

Dimension 2: The stages of development of agentic AI

The second dimension describes where AI systems stand in terms of AI maturity—and where they can develop:

1. Tool (Level 1) – responds to simple inputs without understanding context.
2. Assistant (Level 2) – supports people in fixed roles and workflows.
3. Agent (Level 3) – acts in a goal-oriented manner, makes decisions, and adapts.

Next in line are Professional (Level 4) and Innovator (Level 5) – levels at which AI thinks, learns, and creates new things independently. These levels serve as a maturity model for prioritizing programs and planning expansion stages in a targeted manner.

How we use the framework in practice

The framework is not a theoretical blueprint, but rather an orientation system for transformation. In projects, it serves as a basis for developing programs, architectures, and roadmaps that gradually work toward the next level of agential maturity and helps to:

• Structure organizations before scaling AI
• Introduce governance and standards across teams
• Prioritize use cases in a targeted manner
• Ensure the reusability of skills across projects

To ensure success, the contents of the framework are translated into concrete programs, hero services, and live hacks that have a direct impact on everyday business operations. The framework forms the strategic backbone for this—it combines strategy, technology, and organization into a comprehensive picture that can be implemented.

From roadmap to reality: The path to agentic AI

The path to Agentic AI is not a technical sprint, but a strategic learning journey. The Agentic AI Framework serves as both a compass and a tool—it provides orientation, a common language, and makes AI tangible and implementable.

Companies that take this structured approach build standardized, reusable agent capabilities—automating not only individual workflows, but entire value chains. Only those who start using the application now, develop suitable workflows, and use AI responsibly will remain capable of acting in the next generation.

Find out more in the white paper "Road to Agentic AI" – with a framework, practical examples, and specific recommendations for the next steps.