Insights Blog From task bots to strategic operators: ...

From task bots to strategic operators: Understanding agentic AI Systems

Written by Antonia Mittmann
The most important facts in 20 seconds
  • AI agent ≠ agentic AI system: AI agents handle simple, reactive tasks; agentic systems act autonomously across steps, set goals, and adapt. 
  • Agentic systems require strong governance, oversight, and trust. 
  • Transparency, human oversight, and compliance (e.g. GDPR, EU AI Act) are essential for responsible deployment. 
  • Start small: Focus first on small agentic AI systems where several agents can reduce effort, error, or friction, rather than aiming for full-scale ecosystems right away.  
Agenda

AI agents and agentic systems are no longer niche topics. They’re at the heart of a growing shift in how we build and use intelligent automation. As more teams experiment with autonomous workflows, it becomes crucial to understand what distinguishes a basic AI agent from a more complex, agentic AI system. And these aren’t just semantic differences; they reflect two fundamentally different approaches to intelligent behaviour in machines.

In AI research, an agent is any system that can perceive its environment, interpret information, and act autonomously towards a specific goal. Think of a chatbot that answers customer questions or a script that automates calendar scheduling. These are AI agents: helpful, focused, but ultimately reactive.

Agentic goes a step further. An agentic AI system doesn’t just act on instructions. It sets its own sub-goals, makes decisions over time, and adjusts its strategies as conditions change. The term emphasises initiative, long-term planning, and autonomous behaviour across multiple steps, not just one-off actions.

How do AI Agents and Agentic AI systems differ?

The key distinction lies in scope and autonomy. While AI agents tend to handle one task at a time within fixed parameters, agentic AI systems coordinate multiple agents and tools to pursue broader objectives over time. This difference shapes both the technical design and the actual impact of these systems.

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AI agents shine in scenarios where clarity, repeatability, and speed are priorities. For example, in:

  • automating simple workflows,
  • reducing human error in repetitive tasks,
  • integrating easily into existing systems.

But their limited autonomy makes them rigid. They can’t adapt beyond what they were programmed to do and require regular updates when business logic changes.

Agentic AI systems are designed for complexity. They:

  • make context-aware decisions,
  • learn from interactions,
  • orchestrate multiple agents or services,
  • scale across departments or business units.

The downside? More complexity in design, deployment, and oversight. Autonomy also introduces questions about transparency, governance, or unintended consequences, especially when the intelligent AI system makes decisions that no human explicitly programmed.

How to foster trust in Agentic AI

Simply relying on technical performance is no longer enough. Ethical design, user education, and oversight must also be part of any serious deployment. AI can act as a useful collaborator that handles routine execution, coordinates across systems, and leaves humans free to focus on strategy and creativity. But this collaboration only works when there’s trust. If a system’s reasoning is opaque or unpredictable, it can undermine confidence rather than boost productivity.

With increasing autonomy comes responsibility. Agentic AI systems often work with sensitive data, make impactful decisions, and operate without constant supervision. That makes governance essential.

In regulated environments, like the EU's DACH region, this means:

  • transparent documentation of AI usage,
  • clear data protection policies (e.g., GDPR/DSGVO compliance),
  • human-in-the-loop processes for critical decisions,
  • regular audits and bias assessments,
  • and alignment with evolving legal frameworks such as the EU AI Act.

 

Outlook: Toward Human–Agent and Agent-to-Agent Collaboration

Machines are beginning to act on behalf of humans in more advanced ways, including communicating and transacting with each other. This shift marks the early stages of what is sometimes called the “machine customer” era. But what happens when AI starts shopping? In this future, a voice assistant orders groceries without user input, a car schedules its own maintenance, and an AI investment advisor coordinates with market-forecasting bots.

These are more than just smart tools; they’re digital actors. Systems that:

  • understand their owner’s preferences,
  • interact with other agents (supplier bots, financial services, logistics),
  • and are able to make purchasing decisions, book appointments, or renegotiate contracts.

This next step will require more than just technical capability. It challenges companies to rethink how they design services, structure data, and build trust, not only with users but also with autonomous systems acting on their behalf.

Now, where to start exactly as a business?

For most organisations, the first step isn’t to build a fully agentic, complex ecosystem. Instead, it makes sense to start small, for example, by introducing lightweight agentic AI systems in areas where they can add immediate value. These smaller systems often consist of basic agents designed to automate recurring tasks, assist teams in decision-making, or streamline workflows without major architectural changes.

Common starting points include:

  • repetitive communication tasks,
  • workflows with high error rates or low flexibility, and
  • processes that involve multiple tools or teams.

These use cases are typically time-consuming, prone to mistakes, or require a lot of coordination across functions, making them ideal for initial AI integration.

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Once these systems are running successfully, they can be made more autonomous, better integrated, and more aligned with business goals. The leap from “smart automation” to “agentic collaboration” isn’t just about AI. It’s about architecture, governance, and a clear understanding of what you want machines to decide, not just do.

 

Innovative Agentic AI at Diconium

At Diconium, agentic AI also plays a key role. Our teams have conducted extensive research and development in this area. In addition to our trustworthy and reliable AI chatbots & assistants, one notable result is NEVO – a conversational, agentic AI system designed to support users through natural, goal-oriented dialogue. Unlike traditional chatbots, NEVO helps users navigate complex decisions by guiding them towards suitable products in a more intuitive and efficient way. It can be seamlessly integrated into shopping websites and allows customers to find what they need without relying on filters, menus, or navigating an overwhelming number of options. Whether it’s a new car or bicycle model, or a highly specific product that requires explanation, NEVO supports customers in identifying the right solution for their needs.