Ever tried to hammer a nail in with a potato?

Nor me, but that’s what I’ve felt like I’ve been attempting to do when trying to really understand agents, as well as to come up with an example agent to build.

As I wrote about previously, citing Simon Willison, an LLM agent runs tools in a loop to achieve a goal. Unlike building ETL/ELT pipelines, these were some new concepts that I was struggling to fit to an even semi-plausible real world example.

That’s because I was thinking about it all wrong.

A short series of notes for myself as I learn more about the AI ecosystem as of Autumn [Fall] 2025. The driver for all this is understanding more about Apache Flink’s Flink Agents project, and Confluent’s Streaming Agents.

I started off this series—somewhat randomly, with hindsight—looking at Model Context Protocol (MCP). It’s a helper technology to make things easier to use and provide a richer experience. Next I tried to wrap my head around Models—mostly LLMs, but also with an addendum discussing other types of model too. Along the lines of MCP, Retrieval Augmented Generation (RAG) is another helper technology that on its own doesn’t do anything but combined with an LLM gives it added smarts. I took a brief moment in part 4 to try and build a clearer understanding of the difference between ML and AI.

So whilst RAG and MCP combined make for a bunch of nice capabilities beyond models such as LLMs alone, what I’m really circling around here is what we can do when we combine all these things: Agents! But…what is an Agent, both conceptually and in practice? Let’s try and figure it out.