By Terence Tse
Agentic AI is AI systems with agency. It means they can make decisions and take action as needed. What makes them vital, and how will they change the fundamentals of business operations?
AI has rapidly evolved from predictive analysis to being able to “interact” with us. Now, we stand at the threshold of agentic AI — a breakthrough technique that builds on generative AI (GenAI) but moves beyond responding to prompts. At the core, AI agents proactively make decisions and take actions to achieve defined objectives. This shift can fundamentally change how businesses operate, and humans interact with technology, opening up new possibilities for companies.
What is agentic AI?
As mentioned above, agentic AI is characterised by its ability to execute tasks and make decisions independently towards defined objectives. While traditional AI operates on fixed rules and GenAI focuses on content creation, agentic AI systems are designed to take autonomous action within specified parameters.
The foundation of agentic AI rests on three fundamental characteristics. First, autonomy enables AI agents to execute complex tasks without constant human supervision. When given a high-level objective, such as optimising a supply chain or managing customer relationships, AI can independently determine and implement the necessary steps while deferring to human oversight on critical decisions.
Second, adaptability allows the AI to learn systematically and adapt its performance through outcome analysis and data integration. The system progressively enhances response patterns based on interaction history and success metrics, for example, in customer service applications, evolving to take on increasingly sophisticated operations over time.
Third, goal orientation distinguishes agentic AI from reactive or purely generative systems. When given a business goal, such as “planning my vacation”, AI agents can analyse conditions and requirements, identify opportunities, and develop and propose (or even implement) solutions. This goal-oriented characteristic ensures that actions align with intended business outcomes.
In summary, think of agentic AI as having a highly skilled personal assistant who understands your objectives and proactively works towards achieving them without constant direction—a shift from a “do-it-yourself” approach to one that is “do-it-for-me.”
How does agentic AI differ from GenAI?
agentic AI functions as an autonomous decision-making system, managing complex operational challenges through reasoning, planning, and independent action.
Agentic AI and GenAI serve distinct business purposes within the AI landscape. GenAI, like ChatGPT, specialises in content creation and efficiently produces written materials, code, and visuals based on specific inputs (prompting). This capability has upended the conventional approaches to content production and creative processes. In contrast, agentic AI functions as an autonomous decision-making system, managing complex operational challenges through reasoning, planning, and independent action. These systems handle multi-step processes while adapting to changing conditions and focusing on objectives. As an analogy, GenAI is much like a recipe generator. On the other hand, agentic AI is akin to a personal chef who manages the entire meal preparation process end-to-end, from menu planning to serving.
A work and business transformer
Agentic AI is not as far-fetched as one may think, especially when many AI companies are at full speed in their development. Agentic AI is potentially changing how we work and think about professional roles. Automating routine tasks allows human workers to focus on higher-value activities that require critical thinking, creativity, and emotional intelligence. This, in turn, can lead to more fulfilling work and higher job satisfaction. In addition, this evolution in workplace dynamics creates opportunities for professionals to develop new skills and expertise in AI system management and strategic oversight, fostering a more dynamic and innovative work environment.
Going beyond individual work, AI agents could transform the business landscape. For instance, they can improve customer support by automating routine interactions, answering queries, and resolving issues with speed and accuracy. They can also offer personalised responses and even predict potential problems before they arise. The healthcare sector can benefit from AI agents that analyse medical data, assist in diagnostics, and provide continuous patient monitoring. Such systems support medical professionals by offering real-time data during procedures and suggesting treatment plans, ultimately improving the quality of care delivery.
Automating routine tasks allows human workers to focus on higher-value activities that require critical thinking, creativity, and emotional intelligence. This, in turn, can lead to more fulfilling work and higher job satisfaction.
In the financial sector, companies like Nexus FrontierTech are leading the way. This vendor uses agentic AI techniques to help the lending teams at global banks collect and analyse environmental and sustainability data, enabling them to approve new loans efficiently and monitor existing ones in real-time. The impact extends to supply chain management and manufacturing, where AI agents are being put together to help optimise inventory levels, predict potential disruptions, and monitor equipment performance to prevent failures. In transportation (in the future), autonomous vehicles will use AI agents to process sensor data for navigation, obstacle avoidance, and traffic law compliance.
In general, business operations can find new efficiencies through AI agent systems. My colleagues and I are developing a customisable system for small and medium-sized enterprises (SMEs). It will be capable of handling specific tasks like responding to general and unexpected customer queries or processing invoices into structured data formats, regardless of their style and format. This, we believe, finally offers SMEs a real chance to access otherwise still very expensive AI that usually only large corporations can afford.
While still in its nascent stage of development, agentic AI is already delivering tangible benefits across industries. From streamlining operations to enhancing service delivery and resource utilisation, these intelligent systems pave the way for a more automated and efficient future. As the technology continues to evolve, its applications are expected to expand, creating new business opportunities and improving service delivery across sectors.
Risks and Challenges
Despite the benefits, AI agents present significant technical, ethical, and organisational challenges that require careful consideration and proactive management. Like all machines, AI agents can malfunction, leading to an operational breakdown. AI agents may on their own exploit loopholes in their programming to achieve objectives through unintended shortcuts rather than fulfilling their intended goals.
Agents may also inappropriately apply learned goals to unforeseen situations or appear aligned with intended objectives during training while harbouring different internal goals. Just like GenAI is now being used to write sophisticated and personalised fraud emails, AI agents can be abused to facilitate more elaborate scams by creating more convincing and personalised content at unprecedented speeds. Furthermore, AI agents can be used to automate cyberattacks, posing new and additional security risks.
Beyond technical challenges, accountability remains a crucial issue. When AI agents make mistakes, questions arise about responsibility attribution: Should the AI itself, the implementing company, or the technology provider be held accountable? And how do businesses buy insurance against the financial consequences of such mistakes? Transparency in decision-making processes is equally important, as we need to ensure AI-generated outputs and decisions throughout the process are explainable and understandable.
While automation through AI agents can enhance efficiency, it is crucial to ensure it does not diminish human roles.
Organisations must also consider the human impact of implementing these systems. While automation through AI agents can enhance efficiency, it is crucial to ensure it does not diminish human roles. Experience has shown that one critical factor to successful AI deployment in business is that end users must have both the interest and incentive to take on newly introduced systems. Given that it demands more skills in using agentic AI, companies must ensure that training is given and a technology-orientated culture is in place. The more complex agentic AI will likely require more complicated and more robust regulatory frameworks, clear guidelines and guardrails. These are all essential for building trust and ensuring sustainable adoption of agentic AI technologies.
The road ahead
Agentic AI represents a transformative force in business technology, moving beyond simple automation to autonomous problem-solving. AI agents hold the promise to revolutionise industries by tackling complex challenges with unprecedented efficiency. Companies that successfully integrate agentic AI will gain significant competitive edges, but implementation demands careful consideration. Success requires strategic investment in infrastructure, training, and governance frameworks. As businesses enter this new era of autonomous technology, those that balance innovation with responsible deployment will be best positioned to thrive in an increasingly automated landscape.
