Artificial intelligence is evolving beyond being a reactive tool for recognising patterns. A new wave of AI—known as agentic systems—is emerging, designed not just to respond but to reason. This shift is opening up new possibilities for how AI can engage with complex, dynamic environments.

Beyond Fast and Predictable

Until now, most AI has been built to recognise and respond quickly. Large language models like ChatGPT excel at sifting through vast amounts of data to deliver accurate, rapid responses. They operate on what psychologists might call System 1 thinking—intuitive, almost instinctive. But as good as these models are, they struggle when faced with unpredictable contexts or tasks requiring judgment rather than just recall.

This is where agentic AI comes into play. These systems are designed to simulate and evaluate multiple scenarios in real-time—a process called inference-time compute. Rather than relying on pre-set answers, they can pause to explore different pathways, helping them navigate scenarios that lie outside of their training data​ Sequoia Capital.

The Role of Deliberation in Complex Decision-Making

For areas like cybersecurity, traditional AI can flag known threats but often falls short when faced with new and evolving risks. Agentic AI, however, can engage in dynamic reasoning, assessing potential vulnerabilities as they emerge. It’s not a matter of mimicking human intuition, but of using statistical and probabilistic reasoning to bridge the gaps left by more rigid, rule-based approaches.

In legal research, the implications could be profound. A traditional AI tool might summarise existing laws or precedents, but an agentic system could dynamically reinterpret those precedents in light of new cases. This isn’t just about efficiency; it’s about helping professionals stay ahead in rapidly changing environments by offering real-time insights as legal frameworks evolve​ Mousse Magazine and Publishing.

Building a New Layer: The Reasoning Layer

This shift introduces a new layer to AI—one that goes beyond pattern recognition to adaptive reasoning. While traditional models depend heavily on pre-set responses, agentic AI can actively adjust its approach based on context. This capability could change how organisations engage with AI tools, enabling more thoughtful, adaptive systems.

For instance, project management platforms equipped with agentic AI could not only track timelines and flag risks but also suggest alternative strategies based on ongoing shifts in project dynamics. This added adaptability can help organisations stay agile in uncertain conditions, transforming static reporting into proactive decision-making.

Thoughtful Integration Requires Accountability

As agentic AI develops, the challenge isn’t just technical; it’s ethical and practical. Building systems capable of reasoning brings greater demands for accountability and transparency. With AI increasingly involved in complex decision-making, businesses must ensure these systems are both interpretable and fair. This is especially critical in contexts like legal analysis or hiring, where biases in AI could have serious consequences.

Transparency isn’t a secondary concern; it’s central to maintaining trust in these new systems. Developers need to prioritise clear frameworks that explain how AI systems reach their conclusions, particularly as these tools become more influential in guiding strategic decisions.

A More Collaborative Approach to Work

The real opportunity with agentic AI isn’t just about improving efficiency—it’s about rethinking AI’s role in the workplace. These systems aren’t merely tools for automating tasks; they’re becoming partners that can help navigate uncertainty. As AI grows more capable of reflective reasoning, the focus shifts from repetitive automation to strategic collaboration.

In education, this could mean moving from static, pre-set recommendations towards more personalised learning support. Imagine a tutoring system that, instead of flagging wrong answers, identifies the root cause of confusion and adjusts its guidance accordingly. It’s a more nuanced, supportive way of helping students overcome challenges in real-time.

The rise of agentic systems signals a deeper shift in how AI is conceived and applied. The real breakthrough here isn’t about replicating human creativity but fostering systems that can engage more thoughtfully with the complexity of real-world situations. By thoughtfully integrating these capabilities, businesses can build AI that doesn’t just work efficiently but works well—enhancing strategic thinking, adaptability, and collaborative problem-solving in a world that demands all three.