Generative AI has already changed the way many of us work. Deployed out-of-the-box or used in custom applications, large language models have immense potential in almost every industry. But they are fundamentally reactive and cannot call on other applications to achieve a goal. This is what Agentic AI promises to build on.

What makes it different

At its heart, Agentic AI combines a large language model with a set of tools and a framework for orchestrating actions. Instead of simply responding to prompts, these systems can plan, execute and adapt in pursuit of a defined goal. If you ask a large language model what the weather will be like tomorrow, it will simply guess what the answer is. An agentic system could query a live weather application and return a far more accurate answer because of its ability to act.

More typical agentic systems will call on multiple tools to complete a task. For example, we might have an Agentic AI for scheduling meetings that doesn’t just suggest next steps, but actively checks attendee’s availability, prioritizes constraints and books a time everyone can make.

These systems often use reasoning models, which effectively “think out loud” before acting. That internal reasoning can then be logged, giving users and developers a view into how a decision was made. It is not perfect transparency, but it helps reduce the sense of a black box.

This ability to act based on planning and reasoning is what makes Agentic AI fundamentally different from earlier models.

Real-world adoption

One of the clearest early uses for Agentic AI is automating workflows that are repetitive but still require contextual understanding. A good example is invoice validation. This process often involves multiple steps, tools and approval paths. While parts of it can be rules-based, others depend on nuance such as verifying addresses or compliance checks.

Rather than following a fixed script, an agentic system could assess what actions were needed at each step, call the right tools and decide when to escalate or stop. It would effectively manage a decision tree too complex and too variable for traditional automation.

This is where Agentic AI shows its strength. Instead of building rigid rules for every possible scenario, the system reasons through each case in a way that starts to mirror human judgment. It brings together structure, flexibility and responsiveness, something standard automation tools often cannot achieve.

Key challenges

Trust remains a major consideration. Even if a system performs well, users may hesitate to rely on it, especially when it replaces human workflows. That is why involving end users early and iteratively is so important. Building a base solution quickly, then refining it in collaboration, tends to foster both understanding and confidence.

There’s also the ‘black box’ challenge to consider – we want systems that can show how or why they reached a certain conclusion or performed an action. This is important for scrutinizing decisions and spotting underlying patterns. Fortunately, the agentic approach can make this easier. By logging actions and decision paths, these systems offer greater visibility into how outcomes are reached. That is useful both for debugging and for building user trust.

Getting started

If you are considering exploring Agentic AI, the first step is to clarify the use case. Not every process is a good fit. Tasks that already follow clear, rules-based logic are often better handled with conventional automation. But where judgment is involved, especially for small, frequent tasks, Agentic AI can offer significant value.

You will also need accessible, well-structured data, ideally through a modern data platform. Without that foundation, even the best models will not be able to act effectively.

Agentic AI gives organizations a way to automate tasks that require flexibility, context and repetitive decision-making – the kinds of tasks that have traditionally been seen as boring but necessary for humans to carry out. For many, it is the next logical step in using AI to support work that is too complex for rigid automation but too repetitive to remain manual.