For the last couple of years, especially after the onset of ChatGPT on the technological landscape, the general perception has been that Generative AI holds the key to problems and challenges most organizations face. Even all digital transformation efforts began to embed Generative AI across organizations globally as CIOs and business leaders were swept away by its humongous potential and the virality it had achieved in all walks of the life. But all is not hunky-dory with Generative AI projects.
The wild chase has hit the roadblocks in most of the industries as leaders have not been able to derive any perceived value out of their prioritized initiatives. According to Gartner, at least 30% of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value.
The next fronter is Agentic AI
The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.
According to a Nvidia blog, “Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.”
As opposed to Agentic AI, traditional Large Language Models (LLMs) have only been able to generate content using next word prediction and one-shot generation. And most LLMs lack a mechanism to iterate or review their own output. This method of using LLMs is sometimes called “frozen” model-based generation.
Where previously a mechanism called ‘function calling’ has been used to allow developers to describe the functions and actions that these models take, like performing calculations, or making an order, Agentic AI frameworks can now leverage this function-calling capability, allowing LLMs to handle complex workflows with much more autonomy and generate better quality output.
What could the future of Agentic AI entail?
Agentic AI can be applied across multiple domains to simplify and distribute workloads. For example, in the HR domain, an Agentic AI can be deployed to handle common administrative tasks like recruitment and selection, employee onboarding, employee relations, performance management, training and development, compensation and benefits, HR policy and compliance. This frees up time for human HR professionals to focus on the more complicated tasks that require human input.
Recent research from OpenAI and DeepMind has shown that by utilizing multiple AI agents that can collaborate and coordinate with each other, complex problems across various fields can be tackled more effectively, including optimizing intricate supply chains or simulating molecular interactions to accelerate drug discovery processes.
A great example of AI agents in action is ‘Devin AI’, which according to its creators at Cognition AI, is “the world’s first fully autonomous AI software engineer.” Devin AI has been able to successfully implement a software project fully autonomously, end-to-end, from writing an implementation plan to creating a website. This trend of moving away from simple chatbots that deal with simple questions or tasks, to feature-rich agents that can complete complex tasks autonomously, is genuinely a new frontier – and Devin AI, with its remarkable capabilities, has given us a peek at the art of the possible.
Arun Parameswaran, Salesforce India’s Managing Director of Sales, while elaborating on how Salesforce’s Agentforce platform is set to transform businesses, insisted that leveraging AI for business success with data and trust as the foundation for customer success. Salesforce’s Agentforce is designed to manage customer inquiries, streamline employee tasks, and keep your business running smoothly around the clock. Agentforce agents work independently, relying on your company's data to make decisions and complete tasks without human involvement.
Why has Agentic AI evolved so quickly?
Advancements in reinforcement learning (RL), a branch of AI that focuses on creating self-teaching autonomous systems that learn through trial and error, has made it possible to deploy AI agents in real-world applications. Autonomous cars, industrial robots, and gaming are just a few examples of recent applications. Additionally, the advent of LLMs such as ChatGPT have significantly increased the capacity of AI agents within a very short period – and they’ve done this through their superior ability to understand and generate human-like text or speech and consume a large knowledge base.
Agentic AI has evolved rapidly due to significant advancements in machine learning techniques like reinforcement learning, coupled with increased computational power, allowing AI agents to learn from experience, adapt to changing situations, and make autonomous decisions in real-time, making them much more flexible and adaptable than traditional AI systems.
Key factors contributing to the rapid evolution of Agentic AI:
Reinforcement Learning: This technique allows AI agents to learn through trial and error, receiving rewards for positive actions and penalties for negative ones, enabling them to optimize strategies in complex environments.
Large Language Models (LLMs): The development of powerful LLMs like ChatGPT has provided AI agents with advanced language understanding and generation capabilities, allowing them to interact with humans in more natural ways and perform complex tasks requiring language processing.
Improved Computing Power: The increasing availability of high-performance computing has made it possible to train and run complex AI models with large datasets, leading to more sophisticated agentic AI systems.
Data Availability: The vast amount of data generated across various industries provides ample training material for AI agents, allowing them to learn and adapt to specific contexts.
Research Focus: Growing research interest in developing autonomous AI systems has spurred innovation and development of new algorithms and architectures for agentic AI.
Agentic AI – the new dominant area in AI Innovation
Agentic AI is redefining AI innovation, enabling multi-agent collaboration, iterative self-improvement, and autonomous planning. With design patterns like reflection, tool use, and task planning, Agentic AI empowers organizations to tackle increasingly complex challenges. As this transformative technology continues to evolve, it is set to become a cornerstone of enterprise innovation, unlocking unprecedented efficiency and capability.
Agentic AI is not just the next step—it’s a leap forward into a future where AI systems operate as autonomous collaborators in the modern world.
According to an article jointly penned by Prabhakar Srinivasan (Director of Technology, Innovation and Co-Lead of the AI Practice, Synechron) and Sujith Vemishetty (Lead, Data Science, Bangalore Innovation, Synechron), “Combined progress in several domains has made AI agents the dominant area in recent AI research and development, with the introduction of frameworks like LangChain further enhancing the implementation of these AI agents. LangChain provides various tools for the simple building of LLM-driven applications, supercharging the adoption of AI agent technology.”
They further argue that driving this progress has been the introduction of frameworks like LangGraph, Autogen, CrewAI, and GoEX, that can create full-blown, robust, Agentic AI workflows with multiple agents. This further enables features like Agent-to-Agent Communication, Human-in-the-loop, Caching, Agent Observability, and Task Decomposition. They are critical constructs for the building and scaling of enterprise-ready Agentic AI applications.
Key Features of Agentic AI
Agentic AI introduces sophisticated capabilities that differentiate it from traditional AI models, allowing systems to operate with greater autonomy, adaptability, and efficiency. The following are the four popular design patterns in Agentic AI workflows:
Reflection: This feature enables AI systems to evaluate their own outputs critically, identifying potential errors or areas for improvement. By creating an iterative self-improvement loop, Agentic AI ensures more accurate and robust results over time.
Tool Use: Agentic AI empowers systems to leverage external tools and perform function calls. This capability allows AI to access updated information beyond its training data and execute specific tasks, such as calculations, data retrieval, or order processing, with precision.
Planning: AI agents equipped with planning capabilities can break down complex tasks or projects into manageable steps. They can autonomously determine the sequence of execution, monitor progress, and decide when to terminate tasks upon completion, ensuring streamlined and effective task management.
Multi-Agent Collaboration: Agentic AI systems can utilize multiple specialized agents to tackle various subtasks collaboratively. By coordinating efforts, these agents achieve complex objectives more efficiently, leveraging their individual strengths for seamless execution.
These features make Agentic AI a powerful tool for addressing intricate, real-world challenges, enabling applications that go beyond simple outputs to deliver meaningful, context-aware, and actionable results.
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