Agentic AI...evolution of Generative AI into the more complex workflows. Also, using agentic AI to overcome Gen AI flaws is a game changer...example: AI is not accurate with math...for math calculations you can have the agentic flow offload the task to a calc tool and return an accurate result back into the AI flow...all managed inside your AI solution. Your supervisor agentic AI can determine when this is needed automatically. One example of many. I didn't include this in my attach post. 🔥
Agentic AI in action, I've built it to show real life implementation🔥 I'm no sports expert but we can think about Agentic AI as a sports team (pick your favorite). Each player has specific role and expertise and if we pick basketball as an example, the point guard is a type of supervisor. Point guard passes the ball to each of the four other functional players. Each one of the four players has a speciality and characteristics to best execute that speciality. Agentic AI is the same. You can nominate a "supervisor" agent as the 'point guard' and it will pass instructions and manage the flow with each one of the other ai agent 'worker' players. This is a simplified way to explain what agentic ai is and how it functions. Now, let's switch to what can you do with this? To best explain I've just built a live agentic AI use case for Financial Portfolio AI solution. The goal is to create a Comprehensive Investment Strategy Report on any company the user asks for. As in real life, to achieve this there are 3 different functions involved: 1. Portfolio Mgr 2. Financial Analyst 3. Risk Mgr. Imagine putting this together manually and coordinating millions of requests and a backlog. This is where Agentic AI can help. I've then created AI Agent per each function with the specific characteristics needed for each role. To turn all of this into a cohesive outcome I've added the point guard (aka supervisor). The supervisor coordinates the task and put together a report based on the feedback from each of the workers (AI Agents). In the screenshot you can see exactly how I've built the agentic AI flow and setup. I highlighted the role (supervisor/worker) and function for each of the workers. You may also notice that I gave specific prompt engineering instructions to each of the workers. Think about it as setting the specialty for each of the agents. In my design you may also notice that each worker agent goes out to the internet via Google Search to perform its information gathering for its analysis. After all 3 agents completed their tasks the supervisor then gathers the data and compiles the final Comprehensive Investment Strategy Report. There are endless use cases for Agentic AI, if you are still reading award others by adding a like to this post. 💡