Prefer reading instead? The full article is available here.

Demo agents are easy to build, until they crash mid-execution, lose conversation context, or explode your token budget. In this episode, we explore the three critical mechanisms that transform fragile prototypes into production-grade AI systems. You’ll learn:

* How to optimize state with reducers and caching — managing growing state efficiently through composable update functions and skipping expensive recomputation through intelligent caching strategies.

* How to implement persistence and memory — maintaining state across sessions, preserving conversation history, and ensuring agents remember what they’ve already done to avoid redundant work

* How to build fault-tolerant systems with checkpointers — saving state at every step, resuming execution from any point, and recovering gracefully from failures without losing progress

If you’d rather read than listen, the full article (with code examples, implementation patterns, and debugging strategies) is available on Substack:

👉 Enjoyed this episode? Subscribe to The AI Practitioner to get future articles and podcasts delivered straight to your inbox.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit aipractitioner.substack.com

PODCAST — Beyond the Demo: Building AI Agents Remember, Recover, and Scale (Part 2)