In recent months, Agentic AI has generated expectations that often grow faster than the actual understanding of the technology. For many leaders with limited technical expertise, it becomes difficult to distinguish between strategic vision and operational reality. One way to clarify this is to compare it to the old Excel macros, which automated repetitive tasks with a single command. The difference is that today, Agentic AI takes automation to a much more advanced level and on a larger scale, while maintaining the same essence: simplifying and optimizing processes. Let’s analyze the reasons for this comparison.
Summary
What Agentic AI really is
Agentic AI can be described as software built to pursue objectives by coordinating tasks with a certain degree of autonomy. At their foundation, they wrap underlying AI services, orchestrating models, APIs, and external functions to create automated flows.
The effectiveness of these systems depends directly on the quality and reliability of the services they integrate, since weak foundations reduce their potential while solid ones enhance their value.
In practice, they operate through a cycle of planning, execution, and evaluation, guided by probabilities rather than fixed instructions. Instead of following a rigid script, they attempt to interpret context, select actions, and adapt when conditions change.
The technology brings together different components: natural language interfaces that make interaction more intuitive, connections to external tools that expand their range of action, and reasoning mechanisms that help to decide the next step. The level of independence remains limited, but compared with traditional automation the impression of flexibility is stronger.
For organizations, this flexibility is attractive because it extends automation to situations that are less predictable and more dynamic. Agentic AI therefore represents an evolution, not in the essence of what automation is, but in the way it can be applied across a wider spectrum of activities.
From macros to adaptive automation
Thinking of Excel macros helps to frame the discussion in a simple way. A macro was a sequence of instructions that performed repetitive steps on a spreadsheet: formatting cells, creating reports, or processing data with one command. It reduced manual effort by automating tasks that were always the same.
Agentic AI follows the same fundamental principle of delegation, but with more sophistication. Instead of repeating a fixed sequence, it interprets instructions expressed in natural language, manages variations in the environment, and integrates information from multiple sources. It does this by wrapping underlying AI services and external tools into an orchestrated flow.
While a macro collapses if one condition changes, an Agentic AI tries to adjust by relying on probabilistic reasoning.
Referring to it as a cognitive macro is a way to strip away exaggerated narratives. It highlights that we are still speaking about automation, although with greater adaptability, wider scope, and an easier interface. What remains is a tool: powerful, evolving, and valuable when placed in the right context.
Guidance for leaders exploring Agentic AI
For business leaders interested in Agentic AI, the first step is to resist the fascination of the narrative that surrounds it. The language of marketing can be attractive, but the risk is to enter into projects without a clear sense of direction. A more effective approach begins with an assessment of existing processes and an honest question: which activities would truly benefit from automation?
It is wise to start with projects that are limited in scope and measurable in outcome. This helps to build experience, identify real value, and avoid the frustration of initiatives that are too ambitious in their early stages.
Transparency is also essential. Every adoption should include a clear understanding of the limitations of the model, the quality of the data required, and the resources needed to maintain the orchestration of different AI services over time.
Leaders should also evaluate the quality of the underlying services that an Agentic AI relies on, since weak or inconsistent foundations can compromise outcomes.
By approaching Agentic AI with these principles, leaders can create a path that is both realistic and progressive, using the technology where it fits best and avoiding the trap of inflated expectations.
The essence of Agentic AI remains automation, but in a completely revamped form: today it is smarter, more dynamic, and more interconnected. Share on X
Keeping Agentic AI Rooted in Reality
Agentic AI should be approached with a balanced perspective. It represents a powerful extension of automation, able to streamline processes and introduce new efficiencies, yet it also carries dependencies, limitations, and costs that require careful consideration. Seeing these systems for what they are allows leaders to adopt them with confidence rather than illusion.
Grand announcements can be engaging, but lasting progress depends on measured choices. The most useful perspective is to see Agentic AI as a tool that wraps and organizes existing intelligence while also integrating reasoning and orchestration, creating a practical form that can serve strategy when approached with knowledge and discipline, and whose long-term value depends on the strength of the foundations on which it is built.
Agentic AI can be seen as a step that raises a fundamental question: are we observing a novel intelligence or automation that now integrates reasoning and orchestration in unprecedented ways?
- Original article previously published here
