What is Agentic AI
A recent buzzword in the AI space is Agentic AI. Agentic AI refers to an AI system/design approach that allows one to create and deploy AI agents that work for you. It falls under a category of AI that uses autonomous systems to execute tasks and make decisions without human intervention. Unlike normal robotic operation systems, Agentic AI operates independently and makes decisions in a continuous learning process. Additionally, the AI agents can use multiple AI techniques such as natural language processing (NLP), computer vision, and machine learning(ML).
Behind the Nuts and Bolts
It uses reinforcement learning to learn how to make the best decisions through trial and error. It does this through repeated analysis of external and complex datasets. In reinforcement learning, there exists an inherent reward vs punishment system, so when an agent makes a better decision, it is rewarded(Positive Feedback); otherwise, it’s punished(Negative Feedback). This process gradually improves its decision-making capabilities.
AI agents also utilize Deep Learning. Deep Learning helps Agentic AI to spot patterns and adapt; its neural networks learn from massive and complex datasets, which allows AI to make better decisions in complex environments. This ensures dynamic decision-making over static rule-based learning. Overall, the combination of deep learning and reinforcement learning allows AI agents to adjust dynamically and make decisions in a complex environment with minimal influence from humans.
The Architects of Agentic AI
Startups such as Decagon, Sierra, and Ema are building Agentic AI chatbots that improve customer interactions by employing more conversational and personalized tones. Rox and 11x are startups that are also building AI agents to revolutionize customer service. Even larger companies like NVIDIA are starting to envision a future for AI Agents to work alongside employees to drive efficiency.
Sierra builds custom customer service AI for businesses, Ema is a platform that builds and deploys AI agents that help to automate tasks and increase productivity. Decagon also builds AI agents for enterprise customer support. Decagon allows customers to build and scale AI agents. These agents help to resolve customer issues through task automation.
Agentic AI: Just a Hype or a Real Shift?
While the AI revolution began with Generative AI like ChatGPT, Agentic AI is the latest. The main difference between generative AI and Agentic AI lies in their function. Generative AI is about creation; its AI systems output texts, images, videos, etc. Agentic AI is about smart automation, while it generates outputs too, but it’s mostly about making smart decisions in dynamic environments.
What are the current applications? Agentic AI is being used as the brains behind autonomous vehicles, smart assistants, and robotic automation. Agentic AI is very purposeful. Unlike generative AI, it uses both inputs and considers the objective that it is trying to achieve; it then makes smart decisions while considering dynamic factors in its environment.
So far, more companies are entering the Agentic AI space. Microsft. Google has introduced new tools like A2A(Agent-to-Agent protocol), which lets developers build/deploy AI agents that can work across various frameworks. Microsoft has Azure AI, which provides tools for building AI tools used in the healthcare industry that improve efficiency/workflow. There are many more companies that are working on their own Agentic AI tools.
The Hard Problems No One Talks About
Agentic AI is rapidly revolutionizing smart automation. But certain cautions need to be considered. The first caution addresses ethics: Whose to blame if an AI Agent makes a costly decision on its own? While it’s true that Agentic AI uses a reward system derived from reinforcement learning to better its decision-making skills, it’s still very capable of making very wrong decisions that can be costly to an organization’s revenue, so who do we blame when this happens? As of now, there are no legal systems for semi-autonomous AI Agents, so this complicates things.
The second caution addresses the balance between autonomy and control: How do we find the balance between giving AI agents freedom without losing control of them? The premise behind Agentic AI is its autonomy, but giving too much freedom to these AI leads to unpredictable behavior, while giving too little freedom removes their autonomy.
Final Thoughts: From Buzzwords to Reality
“Agentic AI” is the buzzword in the AI space currently. Software developers use AI agents for task completion and efficiency. Companies also use Agentic AI platforms for customer service purposes. It seems that Agentic AI appeals to a wide variety of audiences, both individuals and corporations alike.
So, what can we expect of the future? As Agentic AI continues to grow, quantum computing could enhance its function by allowing it to process large amounts of data at incredible speeds. Agentic AI will continue to provide data-driven predictive analysis with increased accuracy. In the transportation space, Agentic AI will improve the efficiency and decision-making capabilities of autonomous transportation. The future is bright for Agentic AI, but only if we navigate the challenges ahead carefully.