Gen AI, AI Agent and Agentic AI

Lets get started in knowing what Gen AI, AI Agent and Agentic AI mean and how they are different from one another.

What is Gen AI? When we ask ChatGPT any question, we get the response in the form of text. It can also generate video or audio responses. so we can define Generative AI as a type of artifical intelligence that creates new content - such as text, images, or audio - based on patterns learned from existing data. At the core we have the LLM which can be ChatGPT, Claude.. etc. The LLM is trained on a huge volume of data. These can be wikipedia text, Google books and others.

Artificial Intelligence (AI) greatly influences the way we work, communicate, and our approach to solving problems. Lets get started in knowing about Gen AI, AI Agent and Agentic AI mean and how they are different from one another.

Generative AI addresses the AI systems designed to create new content based on patterns learned from vast datasets. some of the popular ones include ChatGPT, DALL-E and other models that can generate text, images or code from user prompts. Generative AI is reactive - it takes input , like a prompt and generates output in form of text, images or other media. It has mastery at content creation, such as drafting emails, writing articles, summarizing information, and even creating art. However, Gen AI doesn't independently decide what to do beyond responding to specific user instructions. For example, if asked to plan a trip to a destination it gives the information that might include itinerary, weather and other such details. But when asked about the price of the flight ticket in the future, it can't provide responses because of the knowledge cutoff. This is the limitation for a Gen AI with only LLM.

Agentic AI systems are interconnected form of AI where multiple AI agents work together to plan, learn and execute complex workflows autonomously. It is an AI system with the ability to set goals, adapt to changing situations, and take multistep actions without constant human guidance. When the LLM is connected to a API, memory unit and other tools, it is able to get the information and makes decisions and takes action, it becomes an AI agent.

Some use cases for Generative AI are

• content creation: writing articles, codes completion, creating artwork

• Text summarization or translation.

• Conversational AI, chatbots responding to user queries.

AI agents is a system designed to autonomously perform specific tasks or sets of actions, often interacting with the environment or users. These systems are goal-directed but focused on narrow, distinct tasks. They can sense environment changes and respond accordingly and also can operate with partial autonomy but within fixed boundaries. Some examples are -virtual assistants handling calendar scheduling and home automation devices controlling lighting and temperature. Generative AI is heavily prompt-dependent. Its outputs depend on the quality and context of input. It doesn't autonomously initiate tasks but reacts to explicit requests.

Where Generative AI is reactive and content-focused, and AI Agents handle specific tasks, Agentic AI is proactive and goal-driven. A scenario highlighting this would be of managing an entire business process; gathering data, analyzing it, creating reports, communicating with different software tools, and making decisions to optimize outcomes. Some Agentic AI scenarios are of automated business process management systems, AI powered research assistants orchestrating data collection, analysis, and reporting, and Autonomous robots navigating complex environments with minimal supervision.

As AI technologies evolve, understanding these distinctive differences helps individuals and organizations leverage the appropriate tools for their needs. To explain further, consider the following scenarios to use either Generative AI, AI Agents or Agentic AI.

• Use Generative AI for brainstorming, creativity, and content generation.

• Deploy AI Agents for automating routine, repetitive tasks.

• Invest in Agentic AI when complex, adaptive problem-solving across multiple systems and workflows is required.

Agentic AI systems require sophisticated integration of perception, reasoning, learning, and decision-making modules. Ensuring safety, transparency, and ethical behavior while maintaining autonomy is an active area of research. Each of these forms of technologies often work in synchronization with each other - Agentic AI systems may use Generative AI features to generate content as part of their broader autonomous workflow.

With all the innovations happening in Artificial Intelligence, we can infer that while Generative AI excels at producing content based on input prompts, AI Agents bring autonomy to specific tasks within limited domains. Agentic AI pushes the boundaries further by creating systems that independently plan, learn, and execute complex workflows with minimal human oversight. Understanding these distinctions enables better deployment of AI technologies aligned with business needs and technological capabilities.

Guardrail engineering in AI is vital responsible AI deployment, ensuring systems operate safely, ethically, and in alignment with human expectations. By building layered, adaptive safety nets - comprising detection, corrections, and monitoring- organizations can unlock AI's transformational potential while minimizing associated risks.

AI Guardrails act as integrated policy frameworks, technical controls, and monitoring mechanisms that safeguard AI applications, especially those based on powerful models like large language models (LLMs) or generative AI, from unintended risks.

AI Guardrails are important as the AI technologies particularly generative AI and autonomous agents, can produce outputs that are

• factually incorrect or misleading

• Biased or discriminatory

• offensive, toxic, or harmful.

• Violating privacy by exposing sensitive data.

• Noncompliant with regulatory or organizational policies.

Guardrails help limit these risks, ensuring AI systems uphold safety, fairness, legal compliance, and alignment with human values.