Agentic AI: The Future of Automation or a Pandora's Box?
Agentic AI is shaping up to be one of the most discussed technology topics for 2025. From analysts to LinkedIn influencers, it seems everyone is riding the wave of this emerging concept. But what exactly is Agentic AI, and how does it differ from the AI technologies we interact with today? In this article, we’ll break down what Agentic AI is, explore its levels of complexity, relate it to Robotic Process Automation (RPA), and critically assess its potential advantages and challenges.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems capable of acting with a degree of autonomy, resembling human agency. Unlike traditional AI, which often requires predefined rules, specific inputs, or manual intervention to function, Agentic AI can independently assess situations, make decisions, and execute actions.
Let us dive deeper into its key attributes:
Situational awareness
Situational awareness is the ability of an Agentic AI to understand its environment, interpret changes, and adapt accordingly. This goes beyond simple input-output mechanisms.
For example, in the healthcare sector, an Agentic AI system could continuously monitor a patient’s vitals. If a patient’s blood pressure suddenly spikes, the AI might notify medical staff and suggest immediate intervention or administer pre-approved emergency treatments autonomously. Such adaptability requires the AI to synthesize data from various sensors, prioritize actions, and respond appropriately.
This level of awareness is critical for deployment in dynamic and unpredictable environments, such as disaster relief operations or autonomous driving, where real-time decisions are a matter of safety and efficiency.
Goal-driven behavior
Agentic AI differs from conventional automation by focusing on outcomes rather than predefined tasks. This means the system can determine the most efficient way to achieve its objectives.
For instance, in logistics, a goal-driven AI might identify the fastest routes for delivery by factoring in traffic patterns, weather conditions, and package priorities. If an unforeseen obstacle, such as a road closure, arises, the AI dynamically recalibrates the route to meet delivery timelines without external input.
By focusing on achieving outcomes, these systems introduce unprecedented flexibility into traditionally rigid processes.
Proactive engagement
Proactive engagement enables Agentic AI to anticipate needs and opportunities. Unlike reactive systems, which require explicit commands, proactive AI identifies potential issues or advantages before they become apparent.
For example, in customer service, an Agentic AI might detect recurring complaints about a product and automatically recommend improvements to the engineering team. It could also proactively notify customers about solutions, improving satisfaction and reducing support costs.
This attribute positions Agentic AI as not just a tool for automation, but a partner in strategy and innovation.
Levels of Agentic AI
Agentic AI spans a spectrum, from basic autonomous functions to highly sophisticated, human-like systems. Let’s explore these levels in greater detail:
Low-level applications
Low-level Agentic AI applications are already becoming widespread, particularly in logistics and manufacturing. For example, Amazon’s Kiva robots are designed to navigate warehouse floors autonomously, retrieving and transporting items to streamline the fulfillment process. These robots avoid obstacles, optimize routes, and work collaboratively with other robots and human workers.
While their autonomy is limited to specific tasks, their ability to adapt to warehouse layouts and changing inventory levels demonstrates a foundational level of agency. Such systems increase efficiency while reducing errors associated with manual handling.
Mid-level applications
Mid-level applications involve systems capable of managing complex workflows with minimal supervision. A prominent example is Microsoft’s AI-driven Azure cloud services, which proactively monitor IT infrastructure. These systems detect potential failures, optimize resource allocation, and implement corrective actions before disruptions occur.
In a corporate environment, this capability translates to significant cost savings and improved reliability. Unlike low-level systems, these applications can handle a broader range of scenarios, making them suitable for critical operations such as financial risk management or supply chain optimization.
High-level applications
High-level Agentic AI remains largely theoretical but holds immense potential for transformative impact. Imagine an AI system that independently analyzes market trends, devises strategic business plans, and implements them without human intervention. Such a system could revolutionize industries like finance, marketing, and urban planning.
However, these systems also raise profound ethical and technical questions. How do we ensure accountability for decisions made by a fully autonomous entity? And how do we mitigate risks associated with over-reliance on such systems?
RPA vs. Agentic AI: Continuation or Evolution?
To situate Agentic AI within the broader context of automation, it’s essential to understand Robotic Process Automation (RPA). RPA refers to software robots that automate repetitive, rule-based tasks, such as data entry, invoice processing, or customer onboarding. While RPA has transformed industries by reducing costs and improving efficiency, it operates within rigid frameworks.
Agentic AI represents a significant evolution from RPA. Consider a healthcare example:
RPA in Healthcare: Automates appointment scheduling by extracting data from patient emails and populating it into scheduling software. It relies entirely on predefined workflows.
Agentic AI in Healthcare: Dynamically prioritizes appointments based on patient urgency, resource availability, and real-time data analytics. It adapts to evolving scenarios, such as emergencies, without human input.
This evolution highlights Agentic AI’s flexibility and ability to achieve outcomes beyond the scope of RPA.
Advantages of Agentic AI
Increased efficiency
Agentic AI streamlines decision-making processes, allowing organizations to operate more efficiently. For instance, in financial trading, Agentic AI can analyze vast datasets in real-time, identifying patterns and executing trades faster than human analysts. This capability not only saves time but also enhances profitability by seizing fleeting market opportunities.
Adaptability
Unlike static automation systems, Agentic AI thrives in dynamic environments. Consider disaster management: an AI system coordinating relief efforts can evaluate evolving conditions, such as shifting weather patterns, and allocate resources to areas of greatest need. This adaptability ensures better outcomes in unpredictable scenarios.
Proactive Problem Solving
Predictive maintenance systems powered by Agentic AI exemplify proactive problem-solving. These systems monitor equipment for signs of wear, scheduling repairs before failures occur. This reduces downtime, minimizes costs, and extends equipment lifespan—a critical advantage in industries like manufacturing and aviation.
Scalability
Agentic AI’s scalability makes it invaluable for large-scale operations. In e-commerce, for example, AI systems personalize user experiences for millions of customers simultaneously. By analyzing browsing behavior and purchase history, these systems recommend products tailored to individual preferences, boosting sales and customer satisfaction.
Critical perspectives: risks and challenges
Ethical concerns
With greater autonomy comes greater responsibility—but who is accountable for an AI’s actions? The fatal accident involving Uber’s self-driving car in 2018 illustrates the complexity of assigning blame. Developers, operators, and the AI itself all play interconnected roles, complicating legal and ethical frameworks.
The EU AI Act introduces mechanisms to address these concerns by categorizing AI systems based on risk levels. High-risk AI systems, such as those used in healthcare or law enforcement, are subject to stringent requirements for transparency, accountability, and human oversight. This framework aims to ensure that ethical concerns are identified and mitigated before deployment.
Additionally, the upcoming AI Liability Directive seeks to address gaps in accountability by simplifying the process for individuals to claim compensation for damages caused by AI systems. This Directive aims to harmonize liability rules across the EU, ensuring that victims of AI-related harm have clear legal pathways to justice. By complementing the EU AI Act, it adds another layer of protection for individuals while promoting responsible AI deployment.
Security risks
Advanced AI systems are attractive targets for cyberattacks. A compromised Agentic AI in critical infrastructure, such as energy grids or transportation networks, could have catastrophic consequences. The growing prevalence of adversarial attacks, where malicious actors manipulate AI inputs, underscores the need for robust security measures.
The NIS2 Directive, recently adopted by the European Union, directly addresses these vulnerabilities by imposing enhanced cybersecurity requirements on essential service providers. Under NIS2, organizations deploying Agentic AI must implement robust incident response plans, conduct regular risk assessments, and ensure the resilience of their systems against potential cyber threats.
Bias amplification
Agentic AI relies on data, and biased data can lead to biased outcomes. The COMPAS system, used in the US justice system, was found to exhibit racial bias in predicting recidivism. Addressing such biases requires rigorous oversight and continuous refinement of AI models.
The EU AI Act mandates transparency in data usage and bias testing for AI systems categorized as high-risk. This includes requirements for auditing datasets, documenting training processes, and providing detailed explanations of decision-making algorithms. These measures aim to minimize bias and promote fairness in AI applications.
Job displacement
While Agentic AI creates new opportunities, it also displaces existing roles. A 2023 report by the World Economic Forum estimated that 85 million jobs could be replaced by AI by 2025. Balancing innovation with workforce adaptation will be a key societal challenge.
The European Union has proposed initiatives such as the Digital Skills Agenda to address these challenges. By promoting AI literacy and retraining programs, the EU aims to prepare its workforce for the AI-driven economy, mitigating the social impact of job displacement.
Loss of control
Over-reliance on autonomous systems risks creating scenarios where humans no longer fully understand or control critical processes. MIT researchers warn against “black box” systems that make opaque decisions, highlighting the importance of explainability in AI design.
The EU AI Act emphasizes the need for explainable AI, requiring developers to provide detailed documentation of how AI systems function. This ensures that users, regulators, and affected individuals can understand and challenge decisions made by autonomous systems.
Conclusion: techno-realism and the road ahead
Agentic AI represents a transformative leap in automation, promising unprecedented levels of efficiency and adaptability. However, it also introduces complex ethical, social, and technical challenges. As organizations and societies adopt these technologies, a balanced approach—grounded in techno-realism—is crucial. We must embrace innovation while remaining vigilant about its implications.
As Demis Hassabis, CEO of DeepMind, has remarked:
"AI is a powerful tool, and with great power comes great responsibility. It is up to us to wield it wisely and ensure that its benefits are shared by all."
This sentiment emphasizes the need for vigilance and thoughtful governance in the face of rapid technological advancements.
The journey toward fully agentic systems is just beginning, and the discussions we have today will shape the future of this powerful technology. Let’s ensure we get it right.
At consey.legal, we understand the complexities of navigating the legal and ethical landscape of emerging technologies like Agentic AI. Whether you need guidance on compliance with the EU AI Act, understanding the implications of the AI Liability Directive, or mitigating cybersecurity risks under NIS2, our expertise is here to help. Contact us at hallo@consey.legal and let us support your organization in embracing innovation responsibly and ensuring long-term success in a rapidly evolving digital world.
Written by Kris Seyen, Founder & Managing Partner consey.legal
