Agentic AI leverages advanced reasoning and iterative planning to independently tackle complex, multi-step challenges. These AI systems are capable of making decisions, taking actions, and adapting to new situations with minimal human involvement. Unlike traditional AI—which primarily supports passive activities like answering questions or providing predictions—agentic AI acts proactively: it sets objectives, develops and carries out plans, and can even collaborate with other AI agents.
Agentic AI systems distinguish themselves from conventional, reactive AI by their ability to make decisions and act independently to reach their goals. They continuously reason, plan, and adjust their strategies based on immediate feedback from their environment. This is often achieved by combining multiple AI elements, such as large language models (LLMs), to interpret instructions, design plans, and accomplish tasks using various tools and data sources.
Within an agentic AI system, AI agents are specialised components responsible for reasoning, planning, and taking initiative independently. This makes them invaluable for complex real-world applications such as automation, business process optimisation, and scientific research.
There are several business benefits to using agentic AI.
Agentic artificial intelligence empowers businesses to automate a wide range of complex or specialized tasks, reducing reliance on manual, human-driven processes. By handling repetitive and time-consuming workflows, agentic AI allows employees to focus their efforts on higher-value activities, such as solving problems, developing strategies, and driving business growth.
Agentic AI enables businesses to deliver highly personalized customer interactions. Leveraging existing customer information, agentic AI can swiftly generate customized messages, communicate in a manner that matches the customer’s tone, and recommend products that best fit their needs. As a result, agentic AI fosters stronger customer relationships and builds greater trust between your customers and your organization. Additionally, businesses can harness agentic AI to analyze feedback, pinpoint common themes, and relay these insights to product teams. It can even respond directly to customers who provide feedback, helping create a positive cycle where customers feel heard and valued.
Agentic AI is designed to continuously learn and evolve, adapting its approach as new tasks are assigned. Through ongoing interactions and responsiveness to feedback, it refines its decision-making via this iterative process. For businesses, this means the benefits delivered by agentic AI grow progressively stronger, providing increasing value over time.
Agentic AI acts as a powerful collaborator for human agents, boosting their productivity by minimizing tedious manual work. When human agents partner with agentic AI, they can tackle complex problems more effectively, streamline challenging decision processes, and significantly enhance overall efficiency.
Agentic AI systems can be structured as either single-agent or multi-agent configurations. In a single-agentic setup, one AI agent manages all tasks in sequence, making this approach ideal for businesses seeking rapid solutions to well-defined problems or processes. Alternatively, multi-agentic AI consists of several AI agents collaborating to divide complex workflows into manageable components. This model is inherently more scalable and adaptable, particularly when addressing intricate scenarios. Most references to agentic AI in practice describe this diverse, multi-agent format. Below are several structural variations of multi-agent systems.
In a vertical multi-agent system, tasks are organized within a hierarchy, with lower-level AI agents handling simpler responsibilities, while higher-level agents manage more complex functions. The upper tiers take on challenges that demand advanced capabilities and large language models (LLMs), such as critical thinking, reasoning, and making decisions. Meanwhile, lower-level agents are responsible for gathering, formatting, and processing data before passing it upward for further analysis and action.
Although agentic AI and generative AI are both forms of artificial intelligence and can complement one another, they serve distinct purposes.
Generative AI, as the name implies, specialises in producing new content—such as text, images, code, or music—based on the prompts it receives. At its core is the large language model (LLM), which enables generative AI to deliver value through its capabilities and straightforward extensions. With generative AI, you can create or edit content and even perform basic function calls or combine different options in sequence.
Agentic AI is a specialized branch of generative AI focused on orchestrating and managing agents that rely on large language models (LLMs) as their “brain” to carry out actions using various tools. Unlike generative AI, which is primarily concerned with content creation and basic function execution, agentic AI is designed to take meaningful actions within underlying systems, driving toward more complex, higher-level objectives.
For example, generative AI can create marketing materials, while agentic AI deploys them, tracks performance, and automatically adjusts strategies based on results. In this way, agentic AI leverages generative AI as a tool to achieve its objectives.
Agentic AI works by autonomously perceiving its environment, reasoning through complex scenarios, acting on goals by breaking them into subtasks, and learning from feedback to improve over time. It combines multiple AI techniques such as decision-making frameworks, planning, reinforcement learning, and integration with external systems and tools to handle dynamic, multi-step problems without constant human oversight.
Receiving a user instruction, interpreting intent, and possibly asking for clarification.
Planning and allocating tasks into subtasks and executing them autonomously, often interacting with various internal and external data sources and APIs.
Acting through these planned subtasks, ranging from verifying transactions, correcting errors, to updating systems automatically.
Learning continuously from the outcomes via feedback loops to enhance future performance and adapt to changing environments.
Unlike traditional AI focused on single tasks or generative AI that mainly outputs static content, agentic AI integrates autonomous decision-making and dynamic adaptation. It specializes in collaboration across multiple specialized AI agents for deep domain performance, and it implements guardrails for governance and compliance during execution. Overall, it aims to mimic human-like autonomous problem-solving in real-world applications with high flexibility and contextual awareness.
Collaborative AI understands individual habits, preferences, and goals, enabling it to offer more personalized support and make better-informed decisions over time.
Several challenges are associated with agentic AI and building effective models.
The process of building a multi-agent architecture that effectively coordinates with other models, has specific knowledge of how to tackle certain tasks, and can perform high-level reasoning and strategic planning is a challenging task. Agentic AI is a cutting-edge area of technology that relies on numerous challenging AI strategies. Due to the complexity of designing an effective system, many companies will struggle to access an effective version of agentic AI.
Agentic AI works independently and with minimal human intervention. This benefit also makes testing, debugging, and determining where an AI model has gone wrong a challenge. Developers must build traceability and reproducibility into the AI model, with special attention paid to tracing any errors and determining their causes.
Even in advanced AI systems, AI hallucinations can impact workflows, leading to significant errors and problems for the business operating the model. If models generate false information and then relay it to the rest of the AI agents, incorrect data can rapidly spread, escalating errors in the final output. Especially in industries such as finance and healthcare that have severe real-world implications, businesses must have a great deal of trust in their product before using it extensively.
Agentic AI deployments carry risks including operational disruptions from autonomous actions, ethical issues like biased decisions, legal liabilities under evolving regulations such as the EU AI Act, security vulnerabilities like unauthorized data access or privilege escalation, and reputational harm from unmonitored interactions.
Organisations mitigate these through pre-deployment risk assessments, behaviour simulations, and stress testing to map vulnerabilities. Clear boundaries define autonomous actions versus human approvals, with named owners accountable for oversight and escalation triggers. Real-time monitoring, logging, auditing, guardrails, failsafes, and contingency plans like sandbox isolation ensure control and compliance across the AI lifecycle.
Implementing agentic AI in a business requires careful consideration. Here are some key factors to keep in mind:
Businesses should carefully evaluate their needs and resources before implementing agentic AI. They should also be prepared to invest in the necessary infrastructure, expertise, and training to ensure the successful adoption of this technology.