Intro

The previous episode of our agentic series, where we offered a go-to vocabulary, has become our most popular premium subscription post. Nothing surprising – terminology around AI agents is vague and often misleading. People use whatever terms they want, especially in the business world. “Build me a bot!” – a business inquiry – might actually mean a multi-framework agent that connects different databases, provides communication, and performs analysis. While the vocabulary we created is great (though not perfect, of course), we received feedback: “Show some concrete examples to illustrate this.” Thank you, dear reader; you are absolutely right. We couldn’t provide examples along with the vocabulary due to the newsletter’s size restrictions, but we are doing it now.

However, we have to warn you – it will be brief, because to become aware of AI agents, one needs just to look around. We all have been using AI agents for many years now. We can barely imagine our lives without them.

But why do we talk so much more about AI agents now? Yup, blame – or rather thank – generative AI. LLMs enabled a giant leap towards more autonomy and perceived intelligence in AI agents. Now, with multimodal models improving exponentially, AI agents are rising to a new level.

Still, that’s not the only thing that LLMs improved. LLMs have also revolutionized how we interact with AI. Beyond zero-shot prompting, LLMs enable agentic workflows, allowing AI to think*, research, revise, and improve iteratively. (*Please consider all antropomorphizing verbs to be in””). This evolution transforms agents into more adaptable and capable entities.

The pinnacle of these workflows is multi-agent systems, where multiple agents collaborate, breaking down complex tasks into manageable subtasks. This structure enhances control, testing, and maintenance while enabling sophisticated, scalable workflows like parallel or sequential task execution. Although challenges arise, such as the stochastic behavior of LLM agents increasing error risks, multi-agent architectures are a natural evolution. They align with modular development patterns and provide reusable, adaptable solutions, paving the way for the next generation of autonomous systems.

In today’s episode: