Is Agentic AI the next step after LLMs or just better prompt engineering?

All answers to this question.

Think of the LLM as the "brain" and the Agentic Framework as the "nervous system and limbs." An LLM by itself is a stateless predictor; it just answers questions. An Agent has memory, planning capabilities, and the ability to use tools (like a web browser or a Python interpreter). Architecturally, you should look into ReAct (Reason + Act) patterns and "Chain of Thought" prompting. It’s not a new model, but a new paradigm of "Orchestration" where the model is given an environment to interact with, rather than just a text box.

   Answered 2025-11-05 by Cynthia Barrett

Cynthia, that's a clear distinction. But from a developer's standpoint, doesn't this make "State Management" the biggest challenge? How do we keep an agent from losing its "mind" during a long-running task?

   Answered 2025-11-18 by Ryan Fitzgerald

• Ryan, you've touched on the "Context Window" problem. To solve this, we use Vector Databases for long-term memory and "Check-pointing" to save the agent's state. As a Data Scientist, Eric, you should focus on RAG (Retrieval-Augmented Generation) combined with agentic loops. This allows the agent to pull relevant info from a database, "remember" it, and use it to solve multi-step problems without overflowing its immediate memory.

     Commented 2025-11-28 by Cynthia Barrett

It’s basically an LLM with a "To-Do" list and a "Toolbox." It checks the list, uses a tool, looks at the result, and repeats until the job is done.

   Answered 2025-12-05 by Jordan Bell

• Perfectly summarized, Jordan. It's the move from "Chat" to "Do" that defines the Agentic era. That's the core trend to follow for anyone in AI development.

     Commented 2025-12-12 by Eric Sullivan