Five Things You Need to Know About Agentic AI

More and more businesses are turning their attention to Agentic AI because of its vast potential to simplify working processes.

While many people are familiar with Generative AI (GenAI), which reacts to questions or prompts, such as when using ChatGPT, fewer are as familiar with Agentic AI. The term “agentic” comes from the word “agent,” which in this context means something that takes an action.

Agentic AI goes further than GenAI by making decisions or performing tasks autonomously using “agents”. If you ask an agent to do something, it will work to get it done without needing any further prompts or asking any other questions. Additionally, separate agents work together to get things done without needing a human to connect them.

For example, if you asked a “travel agent” using Agentic AI technology to book you a trip to Spain, it would be able to take care of every anticipated step of the trip – the best seat on the plane, upgrading, a king-size bed in a suite facing the ocean at a bespoke hotel, a best-priced table at a chic restaurant – you name it, Agentic AI can handle it.

Here are five key things you need to know about Agentic AI:

1.???? Agentic AI frameworks are more autonomous

In the past, a mechanism called ‘function calling’ has been used to allow developers to describe the functions and actions that large language models (LLMs) take – like performing calculations or making an order. Agentic AI frameworks can now leverage this function-calling capability, allowing these models to handle complex workflows with much more autonomy, generating better quality business output.

2.???? Agentic AI can be applied across multiple domains to simplify workloads

So, in the HR domain, for example, 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 issues. This frees up time for human HR professionals to focus on more complicated tasks that require human input and action.?

3.???? Agentic AI has evolved quickly

Advancements in reinforcement learning (RL), a branch of AI that creates 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 recent examples. With the advent of LLMs like ChatGPT, the capacity of AI agents has significantly increased within a very short period – and this has led to a superior ability to understand and generate human-like text and speech and consume a large knowledge base.?

4.???? Agentic AI is the new dominant area in AI R&D

Combined progress in Agentic AI domains has made AI agents the new dominant area in AI research and development (R&D), 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.?

5.???? Agent-based workflows could be a path to Artificial General Intelligence (AGI)

Andrew Ng, founder of DeepLearning.AI, has said this year that four design patterns for AI agentic workflows (Reflection, Tool Use, Planning and Multi-agent collaboration), will drive massive progress in AI – in fact probably more than the next generation of foundation models like the successors to GPT-4. This could even, in time, lead to AGI, the Holy Grail of AI research. An example of applied AGI could be a personal assistant that uses human-like cognitive abilities to understand and respond to a wide range of tasks and questions using natural language.?

Agentic AI has the potential to transform business, reducing workloads and simplifying processes.

To find out more, read Agentic AI: The New Frontier in Generative AI