There is no universally agreed definition of what an AI agent is. In practice though, several patterns are emerging. These patterns demonstrate the coordination and integration of multiple AI services to build sophisticated Agentic AI systems capable of handling intricate tasks.

These Agentic Systems architectures can be grouped in 2 main categories: workflows, where LLMs and tools are orchestrated through predefined code paths, and agents, where LLMs dynamically direct their own processes and tool usage, maintaining control over how they execute tasks.

Testing these Agentic Systems architectures is a big challenge for the adoption in mission critical scenarios. This is mainly due to their not completely deterministic nature.

The goal of this talk is to give a theoretical overview of Agentic AI in general and these patterns in particular. We will discuss their differences and range of applicability and show with practical examples how they can be easily implemented and tested. We’ll use Quarkus and its LangChain4j extension, but the concepts are universal.