In this episode, we explore how large language models evolved from passive text generators into agentic systems that can use tools, take actions, collaborate, and operate inside dynamic environments. We explain the shift from “knowing” to “doing,” and why this transition marks one of the most significant changes since the Transformer.

We break down what defines agentic AI, how agents plan and act through tool use, and why multi-agent systems outperform single models on complex, real-world tasks. The episode also covers the emerging agent frameworks, real business impact, and the safety and governance challenges that come with autonomy.

This episode covers:

The gap between text generation and real-world action
What defines agentic AI: autonomy, reactivity, proactivity, learning
Tool use as the bridge from reasoning to execution
Agent lifecycles: planning, action, observation, refinement
Single-agent limits and multi-agent systems (MAS)
Popular agent frameworks (LangChain, LangGraph, AutoGen, CrewAI)
Enterprise, science, and productivity impacts
Safety, latency, memory, and responsibility challenges

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk