Artificial Intelligence (AI) has become an integral part of daily life, seamlessly integrating into a variety of devices such as smartphones, laptops, tablets, smartwatches, TVs, and even household appliances like refrigerators. But not all AIs are equal; some are more capable than others. Agentic AI is one of the more recent advancements in the AI space and is expected to see widespread adoption in the coming years. According to Gartner, Agentic AI was used by only 1% of all corporations in 2024, but that number is expected to rise significantly to 33% by 2028. Deloitte expects that 50% of the companies already leveraging generative AI will begin exploring agentic AI by 2027. But what is agentic AI, and why is it expected to be leveraged by a third of corporations within the next three years?
What is agentic AI?
Agentic AI is the next step in the evolution of AI as a productivity and efficiency tool. Essentially, agentic AI software has agency. It can leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), machine learning, predictive algorithms, and more to create AI systems that do more than generate content. These agentic AI frameworks can function as autonomous, iterative, and adaptable digital workforce modules that can execute complex, multi-step, and effort-intensive tasks without needing constant human oversight and input.
Agentic AI can go beyond simply following instructions and start proactively learning, adapting, and solving problems. This makes it more than just a tool; it becomes a digital workforce capable of managing workflows end-to-end.
How is agentic AI different from generative AI?
Generative AI is relatively straightforward in its operation. You set a specific task, and the AI processes the request by examining the input and comparing it to the data in its Large Language Model (LLM). It then generates output based on this data, which can be further refined through additional prompting until the desired result is achieved. The process is primarily focused on a narrow goal, with the AI remaining passive. The setup is largely task-oriented, where the AI performs a well-defined operation and waits for further instructions.
Agentic AI, in comparison, is far more dynamic and goal-oriented. Instead of focusing on one isolated task, it operates by designing and implementing a sequence of steps that work together to accomplish a broader, more complex objective. Agentic AI goes beyond generating outputs—it continuously assesses the situation, makes informed decisions, and adapts its actions to ensure that it meets its end goal. Rather than producing a single output, Agentic AI oversees multiple tasks and processes, all aligned towards achieving a specific purpose. This makes Agentic AI not only more versatile but also more autonomous, capable of executing complex workflows without constant human intervention.
How does agentic AI work?
While the question “How does agentic AI work?” may initially seem complex, the answer is relatively straightforward. Agentic AI operates based on the principles of the ReAct framework, which stands for Reasoning and Acting. This approach enables Agentic AI to reason through complex tasks and take actions based on that reasoning, making it central to its functionality.
There are four primary steps that make up an Agentic AI software or program:
- Deconstructing the task – AI agents process the task and start gathering resources from all available inputs to help execute the task. This might involve extracting data, analysing media, or creating a workflow. The AI sets processes in motion to begin the task.
- Reasoning – Next, the AI uses the cognitive capabilities of an LLM (rather than its generative applications) and creates the cerebral core of the task. The decision-making aspect of agentic AI comes to the fore here, with tools like RAGs, external software, and additional AI agents or processes used to understand the task and plan steps.
- Execution – Once the agentic AI has the processes in place, it runs the workflow and executes the task based on the processes that it has created in the “Reasoning” stage. Compatibility with failsafe procedures and safety checkpoints are also validated at this stage.
- Iteration and augmentation – The final step involves refining and validating the results. This can be achieved through user input, a separate AI agent included for this very purpose, and ingestion of the results to create a validation data loop.
What are the benefits of agentic AI?
So, how does agentic AI benefit businesses? With its ability to handle complex tasks, Agentic AI offers businesses a new level of productivity.
Tasks that are repeatable, even complicated ones, can be easily executed using agentic AI. This includes heavily specialised tasks. Agentic AI can save time and costs by taking on such tasks, freeing up human resources to be allocated where there’s a greater need.
The analytical aspect of agentic AI can also be leveraged to recognise patterns and predict outcomes. This helps businesses make smarter decisions and more informed choices. For example, it can be used in predictive maintenance in manufacturing or proactive customer service responses in e-commerce.
Agentic AI Use Case
To further understand how agentic AI works, let’s look at a practical use case. A procurement manager could leverage agentic AI to help identify when raw materials are at their most cost-effective and automatically order those materials when the price drops below a certain threshold. The AI system can also include parameters like maximum purchase limits to prevent over-ordering, further automating and streamlining the procurement process.
In addition to procurement, agentic AI can be used for human resources management, such as automating the hiring process, or customer service, such as handling customer queries without human intervention.
Challenges of implementing agentic AI
Like all new technologies, adopting agentic AI comes with challenges. These may include:
- Data privacy concerns: Given the ability of agentic AI to access and analyse large amounts of sensitive data, businesses must ensure that their data security measures are robust.
- Integration complexity: Implementing agentic AI requires aligning existing workflows and systems with AI capabilities, which may be complex depending on the business’s infrastructure.
- Skill gap: There is also a need for skilled personnel who can implement and manage these advanced AI systems.
At Infosys BPM, we understand the importance of harnessing the full potential of advanced technologies like Agentic AI. Our comprehensive solutions help businesses seamlessly integrate AI into their workflows, enabling them to automate tasks, enhance operational efficiency, and achieve their strategic goals. With our expertise in digital transformation, we guide organisations through the process of adopting and scaling Agentic AI, ensuring they stay ahead of the curve and maximise the benefits of this cutting-edge technology.
