Imagine an AI agent as a supercharged version of a conventional LLM (Large Language Model), equipped with access to external tools. This advanced agent operates in a continuous loop, constantly evaluating its environment and making decisions on the best course of action and tools to utilize at each iteration.

By utilizing this approach, the AI agent can tackle incredibly complex problems that would be challenging for a traditional LLM application. If you’re curious about how to develop such a cutting-edge agent from scratch, I delve into the details in the article below.

AI agents represent a significant leap beyond traditional chat applications, harnessing the power of external tools to achieve remarkable results. However, it’s essential to note that they may not always be the optimal solution for every scenario.

In some cases, a sequence of more straightforward and conventional functions might be more suitable. Picture this type of solution as a pipeline, where the output of one function seamlessly flows into the input of the next, steadily advancing towards a resolution.

In the upcoming sections, we’ll delve deeper into the realm of AI agents and pipelines, exploring their respective strengths and the types of applications where they truly shine…