A key challenge with designing AI agents is that large language models are stateless and have limited context windows.

This requires careful engineering to maintain continuity and reliability across sequential LLM interactions.

To perform well, agents need fast systems for storing and retrieving short-term conversations, summaries, and long-term facts.

Redis is an open-source, in-memory data store, widely used for high-performance caching, analytics, and message brokering.

Recent advances have extended Redis' capabilities to vector search and semantic caching,

which has made it an increasingly

popular part of the agenic application stack. Andrew Brookens is a principal applied AI engineer

at Redis. He joins the show with Sean Falconer to discuss the challenges of building AI agents,

the role of memory in agents, hybrid search versus vector only search,

the concept of world models, and more. This episode is hosted by Sean Falconer. Check the show

notes for more information on Sean's work and where to the show.

Thank you.

Thanks for having me.

I'm a big fan, so this is fun.

Nice.

Yeah, well, I'm glad you could be here, why we can work it out. Always good to have a fan on the show as well.

Absolutely.

So I wanted to kind of start with the big picture or a big picture question.

A lot of people are saying that 2025 is going to be this breakout year for AI agents. It's, you know, the year of the agent.

There's a lot of hype going on in the market right now.

We're moving beyond just basic chat.

So from your perspective, what makes building these more autonomous agentic systems hard?

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