The rise of AI systems that can think, plan and act on their own
Artificial intelligence has been on the rise for years. We use AI in search engines, recommendation algorithms, language models and chatbots – often without realizing it. But the way we interact with AI is changing quickly. More and more, we no longer see AI as just a smart tool, but as an active partner. Welcome to the era of agentic AI.
In this blog, we explain what agentic AI actually is, how it differs from traditional AI and why this technology will have such a major impact on how we work, create and make decisions.
The shift: from reactive AI to autonomous agents
Most AI systems we know today are reactive. You ask a question, the system provides an answer. Or you feed in data and the algorithm predicts an outcome. Think of ChatGPT, Netflix recommendations or spam filters: useful, but always dependent on human input and direction.
Agentic AI takes things a step further. These systems function as autonomous ‘agents’: digital entities that receive an assignment, plan how to execute it and then take action on their own. They are no longer just reactive, but goal-oriented, independent and adaptive.
This means that an agentic AI does not need to be guided at every step. You give it a goal and it figures out the best way to achieve it – including gathering information, making decisions and adjusting when necessary.
Important to note: in practice, we almost always build in a “human in the loop”. An agent rarely operates completely autonomously. However, you can gradually give it more freedom as the system earns trust and delivers better results.
What makes an AI ‘agentic’?
To function as an agent, an AI needs several specific capabilities:
1. Goal orientation
An agentic AI works toward an end goal. It doesn’t just get a task (e.g. “generate a text”), but a broader assignment (e.g. “develop a content strategy for target audience X”).
2. Reasoning and planning
The system can analyze complex problems, break tasks down into steps, set priorities and outline an approach.
3. Autonomy within boundaries
Agentic AI operates independently. It makes decisions without constantly asking for input, but always within predefined limits (such as business guidelines, ethical principles or technical constraints).
4. Long-term memory and context awareness
Whereas many classical AI systems are limited to a single session, an agentic AI can retain information over time and use context to adapt its behavior.
5. Adaptability and self-correction
The AI learns from feedback or changing circumstances and can improve itself while working.
A concrete example: from prompt to project manager
Suppose you ask an AI to help with a product launch. A traditional AI system could help by generating some slogans or drafting an email. But an agentic AI goes much further.
Give it the assignment “Ensure a successful launch of product X to a young audience” and the system will:
Decide which channels are suitable (TikTok, Instagram, email, influencers…),
Develop a plan with deadlines and deliverables,
Generate and distribute the right content across channels,
Analyze results and adjust where needed,
And keep you updated with summaries and reports.
Success conditions: this only works if the agent has enough context about the company, the target audience, the channels and past campaigns. In addition, you build in checkpoints: for example, human approval before going live, budget caps and logging for audit & compliance.
The result is a digital project manager or growth marketer who takes initiative, but never acts outside the agreed boundaries.
What does this mean for organizations?
The rise of agentic AI has major implications for businesses, governments and organizations. By allowing AI systems to plan and execute independently, we can:
Work more efficiently, as repetitive and complex processes are automated;
Innovate faster, as AI continuously tests and improves alternatives;
Make better decisions, thanks to ongoing analysis of context, data and feedback;
Free up people to focus on creativity, empathy, and strategy.
But with that power also comes responsibility. If AI systems are allowed to act independently, we must set clear boundaries. How do we ensure they operate within ethical and legal frameworks? How do we maintain control without stifling innovation?
Agentic AI is the next step
Agentic AI marks a new phase in what AI can mean for us. These are no longer passive assistants, but active digital colleagues who can think, build and evolve alongside us – provided they are given the right context and operate within clear boundaries.
In this blog series, we will take you step by step through the different types of AI – from generative AI to agentic AI. Each type has its role, its capabilities and its limits. Together, they form the building blocks of the future of work, innovation and technology.
Curious how this technology can be applied in practice? Or how your organization can start experimenting with agentic AI? Follow our series – or get in touch with us for an exploratory conversation.
