3 misconceptions on Agentic AI

It’s a familiar experience: you’re browsing online in search of the ideal product when a question arises. Rather than calling the company’s customer service, your first point of contact is an online chat via an AI agent. This common interaction is a clear and simple example of Agentic AI, which refers to autonomous systems that can proactively make decisions, give advice and solve problems with little human help. Despite its growing presence, not everyone understands what AI truly is. In this article, we’ll explore three common misconceptions and explain why they don’t hold up against how Agentic AI actually works in practice.

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Agentic AI will spiral out of control and make controversial choices 

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A common misconception is that once Agentic AI is given autonomy, it will escalate uncontrollably and start making reckless or even harmful decisions by going rogue. This fear often comes from the fact that AI is built on systems that cannot understand the question in a true way, and are non-deterministic in nature (in other words, AI generates different responses to the same questions). Randomicity cannot equal true reasoning or understanding. The randomicity, autonomy and not understanding is a recipe for disaster.

The actual risks associated with Agentic AI are less about it being uncontrollable, but more about how its objectives are designed and monitored. Its behavior is shaped by the goals, safeguards and control mechanisms that we humans provide. The true challenge therefore lies in continuously evolving the alignment between Agentic AI and organisational expectations, especially as agents learn, adapt, and occasionally produce unexpected results.

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To illustrate this, consider an AI agent created for social media to maximise engagement. If its reward function is poorly designed, it may spread sensational or misleading content simply to achieve higher clicks. But with well-structured goals that emphasise accuracy, relevance, and trustworthiness, it can create meaningful content without spreading misinformation. Without clear mechanisms for oversight these missteps can become even worse when multiple agents interact, with bottlenecks, conflicts, or duplicated effort as a potential result.

Similarly, an agent assisting in autonomously answering customer service questions can be given the tools to give out coupons or discounts, but, just like their human counterparts, should be monitored continuously to make sure that these discounts are given out at the right time and for the right reasons. And if not, the instructions should be modified and adjusted. Similarly to the service representatives that receive additional training. 

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Agentic AI will replace human jobs

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The rise of agentic AI is often portrayed as a threat to human jobs, with predictions of mass job replacement and shrinking opportunities for employees. While AI agents indeed excel at automating routine tasks, organisations are not approaching them as a blunt tool for job replacement. Instead, HR leaders view Agentic AI as a tool that accelerates redeployment and reskilling. A Salesforce survey of 200 global HR executives found that nearly a quarter of employees are expected to transition into new roles as AI agents take on repetitive work, while most workers (61%) will continue in their roles with AI support. Rather than shrinking the workforce, companies anticipate a productivity boost of 30% per employee, creating space for more strategic, creative and human-centered contributions.

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Taking staffing and recruitment as an example, AI agents can already handle time-intensive tasks such as screening applicants or scheduling interviews. For many teams, which are often already stretched thin and understaffed, this means immediate relief: AI reduces the burden of repetitive work and helps them keep pace with demand. Importantly, this doesn’t eliminate the role of recruiters. It rather empowers them to redirect their energy toward higher-value activities: building relationships with candidates, exercising judgment, and guiding complex hiring decisions. Executives across industries see this as an enormous transformation: a digital labor revolution where jobs don’t vanish but evolve.  The future of work will not be defined by mass replacement, but by a shift toward roles where distinctly human qualities (adaptability, creativity, empathy, and oversight) become more valuable than ever.

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Agentic AI is too dangerous for company security and will cause data leaks

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Furthermore, it is frequently believed that bringing Agentic AI into a company could result in security breaches and spreading of sensitive information. While these systems do expand the risk surface, the real issue again lies in how they are managed and configured and not in the technology itself. Data leaks usually trace back to three issues: shadow AI, overextended access permissions and lack of oversight. With strong safeguards (such as microsegmentation, strict role-based access controls, and continuous monitoring) organisations can significantly reduce these risks and keep AI systems aligned with corporate security standards.

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Take, for instance, an AI-powered chatbot used at a law firm. Without restrictions, it could pull confidential case details into client conversations. But under a governance model where each agent has unique credentials, limited permissions and monitored behavior, that same tool is a secure productivity tool. When properly controlled, Agentic AI strengthens operational efficiency while respecting data protection requirements.

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Gen25 Case Studies: turning AI potential into business results

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At Gen25, we don’t just talk about the potential of Agentic AI: we realise  it! Together with Boat Bike Tours, we implemented Agentforce to overcome peak-season bottlenecks, automating large parts of the quoting process and reducing processing times by more than half. For TCC, we developed a sales support agent that gathers customer information and recommends next best actions, enabling the sales team to engage more effectively and close deals faster. Moreover, we launched CoCo with Corendon: a Salesforce Knowledge-powered chatbot that provides instant and accurate answers to customer questions. CoCo is already delivering great results as it is reducing call volumes and freeing service teams to focus on complex cases. Finally, we rolled out Co. at de Bijenkorf, an internal AI agent that supports employees with instant and knowledge-based responses, reducing handling times and ensuring consistent service quality. These projects show that Gen25 successfully brings Agentic AI into practice to deliver measurable improvements.

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Ready to harness the power of Agentic AI for your organisation?

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Is your business ready to either improve Agentic AI usage or to start using it to optimise performances, Gen25 can help you turn Agentic AI into a reliable advantage. Get in touch with us now!