The evolution of AI in the past few decades has been incredible-from simple machine learning algorithms to deep learning and predictive analytics in complex systems. From here, AI took a great deal of progress with the help of predictive AI, which would forecast based on past information, to Generative AI, where machines learned to generate new content-be it text, images, music, or even code.

Now, we are entering into a new phase in AI's evolution: Agentic AI. This is the next frontier in AI technology—where AI systems not only make decisions but also autonomously execute tasks, interact with other AI agents, and manage complex processes without constant human oversight. Agentic AI promises to reshape industries through the creation of intelligent agents that can autonomously do actions, improve efficiency, and augment human productivity levels in ways we haven't seen before.

In this blog, let's explore what Agentic AI is, how different it is from traditional AI systems, the market trends driving the growth of this technology and its applications in IT.

Agentic AI: What does it mean?

Agentic AI is a major step forward in AI technology, building on the foundations laid by predictive AI and Generative AI. Traditional AI systems are designed to analyse data, make predictions, or generate content. However, Agentic AI goes a step further by enabling systems to take autonomous actions based on their analysis, decisions, and interactions with other AI agents.

Unlike previous generations of AI, which mainly served user inputs or generated on-demand content, Agentic AI systems can:

• Autonomy: Agentic AI systems can act independently to manage and perform tasks usually requiring human supervision. Their design is based on actions taken according to the information they gather, making processes more efficient and less reliant on humans.
• Real-time decision making: Agentic AI systems run data continuously in real time, making decisions and carrying out actions in real time. The ability to do this lets them respond more quickly to dynamic environments or challenges.
• Adaptability and learning: The systems learn from their experience, based on feedback change the behavior, improve performance in time, and become smart in their decision-making processes.

This interaction, deciding, and execution capability sets Agentic AI apart from the traditional AI systems and opens the door to far more dynamic and efficient business operations.

Market trends driving the growth of agentic AI

The rapid acceleration of the digital transformation in various industries has led companies to seek avenues for efficiency enhancement, cost-cutting operations, and decision-making. Several key market trends are driving the growth of Agentic AI:

1. The need for Hyperautomation - Hyperautomation drives the adoption of Agentic AI. As businesses try to automate more complex and end-to-end processes, traditional automation tools alone are no longer enough. Agentic AI enables autonomous systems which can interact with each other, make decisions, and take action across entire workflows, driving full-scale automation.
2. Real-time decision-making - In today's fast world, the decision-making process needs to be quick and based on real-time data. Traditional methods of decision-making often involve delays due to human intervention or manual processes. Agentic AI eliminates these delays as autonomous systems can make decisions in real time and respond to changes in the environment instantly.
3. AI integration across all industries: Industries such as IT operations, cybersecurity, finance, health, and manufacturing are integrating with AI solutions to make processes easy. Demand for AI agents to specifically execute specific tasks such as IT infrastructure management, network security or optimization of customer services grows with each passing day. This growth is attributed to agentic AI, because the latter can independently address complex multifaceted activities.
4. Collaborative AI: Collaborative AI, where different AI agents work together to solve problems and improve operational workflows, is becoming more prevalent. Instead of relying on isolated AI systems, businesses are leveraging multiple AI agents that can communicate, share data, and take action together. This enhances overall system performance and scalability, especially in multi-agent environments.
5. Emergence of multi-agent systems: As the complexity of challenges faced by businesses grows, multi-agent systems are becoming more and more crucial. It is a system where multiple autonomous agents work in parallel, each responsible for different aspects of a task but collaborating to achieve a common goal. This collaborative nature of Agentic AI offers unparalleled flexibility, scalability, and resilience.

How agentic AI functions

Agentic AI works in a multi-step manner:

• Perceive AI agents collect and process information from different sources, extracting useful features and detecting relevant entities.
• Reason A large language model (LLM) is the reasoning engine, comprehending tasks, creating solutions, and managing specialized models. Methods such as retrieval-augmented generation (RAG) are employed to tap into proprietary data stores and provide accurate responses.
• Act Agentic AI communicates with external software and tools through APIs to perform tasks according to its plans. Guardrails can be used to enforce tasks are being performed properly.
• Learn Agentic AI learns constantly through a feedback loop.

Most agentic AI systems consist of several LLMs that interact via prompts, access external tools, and read and write files. One typical configuration is to give different models different roles, like a task manager that decomposes complex issues and assigns subtasks to other models. Agentic AI tends to run asynchronously, enabling several models to work on different aspects of a problem at the same time.

IT applications of agentic AI: A rapid outline

Agentic AI is transforming IT through autonomous, intelligent systems that optimize operations, improve security, and fuel innovation. Here's how:

• Autonomous IT Operations (AIOps): Tracks, identifies, and fixes IT problems in real-time without human action.
• Cybersecurity: Identify threats, automates response to incidents, and adjusts defences to combat emerging threats.
• IT Service Management (ITSM): Automates tickets, offers virtual IT assistants, and maximizes service delivery.
• Cloud management: Dynamically allocates resources, optimizes costs, and manages disaster recovery independently.
• Software development and DevOps: Automates code review, CI/CD pipelines, and incident management.
• Data management: Integrates, analyzes, and governs data in real time for actionable insights.
• Network management: Monitors, optimizes, and secures networks independently.
• IT asset management: Tracks assets, ensures license compliance, and manages lifecycle processes.
• User experience: Personalizes dashboards, provides proactive support, and adapts interfaces in real time.
• Strategic IT planning: Provides capacity planning, technology roadmaps, and cost-benefit analysis based on data.

Conclusion

Agentic AI is not merely the next phase of AI development—it's a revolutionary force that will reshape the Generative AI paradigm. By marrying the generative capability of Generative AI with the autonomous decision-making and execution ability of Agentic AI, we are ushering in a new era of intelligence and innovation.

Companies that adopt this technology will have a huge competitive advantage, unlocking new heights of efficiency, creativity, and scalability. The future of AI isn't about creating content—it's about building intelligent agents that can think, act, and evolve. Agentic AI is here, and it's going to transform everything. Are you ready for the revolution?