1) Prompts

Most people are experienced with prompts. The prompt represents a question, an instruction, a challenge, and so forth you are sending to an AI like ChatGPT. It’s a single request to the LLM for an AI. If you want to forward the content via email, you need to cut and paste it. Those prompts can’t request access to a CRM system or a weather station. As the name indicates, ChatGPT is a chatbot, not more and not less. AND – it was the way to open up AI to humankind on a global scale.

2) Agents

To overcome the technical deficiencies and provide access to email, IT systems, general APIs and databases, functions, and data models, more than a better prompt was needed. A technology infrastructure was needed to access those systems and return a value based on the request. Moreover, we want to connect multiple requests to allow more complex tasks, and we want some of the tasks to be completed right after a previous task is completed without any human interaction. With that, agents need to be able to run autonomously.

In the business world, an Agent is like another service employee who can process entire projects. Instead of replacing precious humans, we let humans manage the agents and focus on more creative and human-like jobs that may be more fun and require a purposeful operation.

3) Solutions

To take the agents a step further and build entire enterprise solutions, we need to prepare one more step: Making Agents interoperable when many agents can interact with each other, collaborate, and share information and tasks as well as processes. An Agentic AI Solution is a series of integrated Multi-Agent clusters designed for specific jobs where possibly hundreds of employees and agents can work together.

4) AI Infrastructure

Those Agent networks, small or large, need a well-thought-out infrastructure. The global web has a so-called stateless world. You click and get something back, but nobody knows whether the task was completed, the page arrived, or the user is still reading. In the business world, processes, people, and agents need to know where things are. Therefore, we need a so called-stateful architecture. In addition, we need memory management where content, data, instructions, and results are stored during the process. As AI can be far more complex than linear code processes of conventional software, Agentic AI infrastructure requires methods that allow lateral or non-linear processes, parallel processing, and more. With all the connection activities, another connection became super relevant: Intra-agent as well as inter-agent protocols. There are more things to communicate than data. Agents need to validate each other’s characteristics, instructions, and navigation data. Those infrastructure requirements are the main reason for building an Agentic AI Framework.