Agentic AI: A new paradigm for Enterprise Applications

The most defining promise of artificial intelligence has been its ability to enable systems to operate autonomously. To act independently on their own with no human supervision. AI based systems are used in almost every domain but specifically within the context of Enterprise Applications they are at the core of how the landscape is changing fundamentally.

As things are evolving at a rapid pace in Generative AI development, “Agentic AI” is taking the center stage. It is starting to have an impact on how enterprise applications are being built and used, allowing users to interact with the applications in a more fluid way via natural language vs traditional interfaces and code. The new GenAI platforms, O/S, toolsets, APIs and frameworks - to build custom AI agents - are poised to fundamentally change how organizations are looking at incorporating an AI driven approach for their enterprise applications.

Evolution:

Agentic AI signifies a substantial evolution in how AI solutions and services are delivered and integrated into various business functions and processes. With large language models (LLMs) at its core, Agentic AI is driving business process automation to be more efficient, scalable, flexible, user friendly and cost-effective. Leveraging the power of foundation models (and other tools in the tech-stack), the AI agents enable systems to work independently - making decisions and performing tasks on their own, analyzing data and creating content - allowing all kind of users (and not just technical experts) to interact with the systems in natural language.

Autonomy:

Unlike regular chatbots - AI agents can act autonomously through self-prompting mechanisms; connect with external systems and APIs, interact with other tools and agents, gather data from different sources, trigger events and execute actions without any human intervention. Leveraging the generated output from LLMs, AI agents can call upon other tools or APIs and manage sub-tasks as defined by the overall business process workflow. Beyond the data sets on which the models are trained, AI agents can access data from various external sources through RAG techniques. Driven by sophisticated reasoning from the LLMs – Agentic AI systems can gather the resources that are available and (autonomously) write queries, generate scripts or sub-routines, call APIs or remote functions to trigger an action. Finally, thanks to their advanced memory capabilities, AI agents can retain and utilize the information from previous interactions to inform future decisions.

The concept of Agentic AI is a significant leap forward in artificial intelligence, combining autonomy, sophisticated reasoning and memory to deliver highly effective and flexible solutions. Enterprises are still in early stages of adoption, but unlike traditional process automation techniques, Agentic AI has the potential to enable dynamic and adaptive workflows that can respond to changing conditions in real-time. It represents a new era of intelligent automation, where AI systems are not just tools but proactive participants in business processes, driving innovation and transformation across industries.