From Tools to Agents: What Agentic AI Means for Us

Following generative AI, a new type of AI is emerging: agentic AI. Unlike generative AI systems, agentic AI systems have the capability to interpret information, reason, and then take action. In other words, it acts as a fully autonomous agent.

This article will explore why agentic AI is the future of artificial intelligence, and its implications for the business industry.

Beyond Prompts: How Agentic AI Acts Autonomously

Agentic AI goes a step further than generative AI as we know it today. While generative AI only answers to text prompts written by users by generating content, agentic AI has the unique ability to execute actions autonomously, it can plan, make decisions, and carry out tasks and coordinating tasks without constant human guidance.

Imagine you are at work on a Monday morning, and you have plenty of emails in your inbox, but you don't have time to answer them all. Instead, you might ask your AI agent to answer them all through one single prompt! A full paragraph of instructions where you tell him what to respond to, which ones to put in the trash or to transfer, and tag the ones that need a follow-up. All with minimal human supervision!

To perform tasks, an agent builds its reasoning on breaking down targets into multiple sub-tasks. Furthermore, he has the ability to operate multiple tools and software programs. To improve himself, an agent learns progressively and adapt his actions based on user's feedback.

Agentic AI Everywhere: How Companies Are Leveraging Autonomous Systems

If you come to a data/AI exhibition this year, you will notice one thing: agentic AI is present everywhere: demos, signs, conferences, ... This is simply because organisations became aware of the benefits of using AI tools for their business and, more specifically, for their employees, since they have started to implement generative AI within internal processes and allow collaborators to gain time over the day.

In addition, major companies are investing massively and actively in agentic systems. For example, Meta acquired AI agent start-up Manus for reportedly 2 billion dollars at the beginning of 2026! On the other hand, Microsoft scales its offer after they announced Agent 365, built to enhance workers' productivity through autonomous actions. Lastly, in their reports, Google confirmed AI agents might create business value, as they are now able to understand and achieve goals. The firm from Cupertino sees agentic workflows as a core part of business processes in a near future.

Consulting firms historically pushed through major investments and key partnerships to accelerate agentic AI adoption. The Big Four (KPMG, Deloitte, PwC, EY) released each multiple AI agents and use them internally. They are now deploying them into clients' environments.

From Analysis to Action: Data Challenges in the Age of Agentic AI

From a practical perspective, AI agents give employees the ability to access, data that has been collected, transformed and analysed autonomously, allowing them to make faster, more informed decisions, focus on higher-value tasks, and leverage insights without needing to manually manage complex data processes.

However, to ensure an agent's efficiency, organisations must ensure that the data used to train the model is clean, consistent, and well-structured; otherwise, the AI may provide poor support, make incorrect decisions, or generate unreliable insights, which could negatively impact business outcomes.

More generally, the autonomous aspect required organisations to evolve their data governance policy within their company: Who monitors AI actions on data. Indeed, errors in data can propagate automatically into decisions. To prevent these situations, businesses need real-time monitoring, auditing, and compliance systems.

The integration of AI agents pose numerous challenges as they use multiple data sources (CRM, ERP, analytics platforms). This means that companies must break down data silos, so agents can access the data they need.

Until now, with generative AI, data was mainly used for insights and reporting. Agentic AI requires from organisations to anticipate their actions on data.

Agentic AI and the Human Factor: Collaboration, Culture, and Accountability

As you now understand, agentic technology is designed to assist humans in their daily tasks, allowing them to focus on higher-value activities. In practice, this means that some decisions shift from humans to AI agents. Employees must adapt to AI-driven workflows, learning to trust these agents while maintaining oversight. Success in this environment requires a mindset centred on collaboration between humans and AI, rather than one of strict control.

This means a change of role for humans, they transition from doing tasks to supervisors of AI actions. In this vein, organisations must train employees to enable oversight, validation, and decision-making. Finally, for employees to rely on AI: Agents must be transparent about their actions by explaining why they took these.

Another benefit of using AI agents is the ability to rapidly test solutions and adjust strategies. To benefit from this, a culture of experimentation must be adopted.

The ability of AI agents to make autonomous decisions raises important ethical and accountability questions. For example, who is responsible when an AI-driven action goes wrong, and how can organisations ensure fairness in these automated decisions while avoiding bias?

In conclusion, agentic AI is a technology with great potential to enable humans to focus on valuable tasks by performing autonomous actions. However, it presents certain challenges in terms of data governance, notably accountability, and the need for a cultural shift and robust adoption strategy to facilitate proper collaboration between humans and AI agents.

‍