The Third Wave of AI

The very first wave of AI was predictive AI. It enabled businesses to forecast trends and make businesses data driven. The second wave of AI consists of generative AI which generates content (text, images, voice, code) and facilitate conversations with humans. Time has come to recognize the third wave of AI — agentic AI. Here AI systems become autonomous to execute tasks. They can also interact with other AI agents.

Agentic AI is different. Here the AI agents go beyond prediction and generation of content. They are action oriented. They can interact with other AI agents. They make decisions (within defined parameters). They can execute complex tasks. Agentic AI can automate the entire tasks. They can perform on behalf of us. It is a significant leap.

AI agents can staff the workplace. They can play the assigned roles. They augment human capabilities. These agents can be trained. There could be co-ordinators of AI workflow. There are team managers. There could be automation and there could be a hybrid model of AI agents and humans.

AI agents are useful in customer service. AI agents can act as returns manager for e-commerce firms. AI agents can make a deal with a car rental company. AI agents can prepare a medical history pf a patient. AI agents can settle insurance claims. In finance, they can resolve complaints.

These agents must work as a team. They can attend meetings. They can contribute by giving valuable inputs. AI agents and humans could have a blurred boundary.

There could be errors of judgement. Agents can err. It is disastrous. Thus. guardrails must be set up. Humans must have oversight over agents — essentially must exercise control over critical points.

The relationship between AI agents and humans is evolving. In future, every executive would have his own agents. Organisations can have specialized agents. There should proper delegation to AI agents.

Work environment has to be reimagined. Workflow must be broken up to be divided between humans and agents. We have to collaborate with AI agents in future.

To sum up, predictive AI focused on historical data to forecast future outcomes. Generative AI created new content, e.g. ChatGPT for text, DALL-E for images and AlphaCode for programming. Agentic AI focuses on autonomous decision making and environmental understanding. It sets and pursues goals. It adapts to dynamic environments. It acts with minimum human intervention. The examples are personal digital assistants, robots managing supply chains, or software trading autonomously in financial markets.

Agentic AI is a significant leap since it shifts from assisting humans to acting independently. Of course, there are issues of safety, accountability and societal impact. After all, it is a matter of balancing innovation with regulation.

Agentic AI system has a workflow — a process where it autonomously plans, decides and executes actions to achieve certain goals in a given environment. Traditional workflows are pre-defined and static. Agentic AI workflows are dynamic, adaptive and aware of the context. The process requires decision making and action without human intervention.

Microsoft’s Agentic Cookbook focuses on implementing generative AI agents using tools like AutoGen. It is a practical guide for developers looking to integrate Agentic AI to workflows (GitHub).