ManningBooks

ManningBooks

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Agent Design Patterns (Manning)

AI agents are showing up everywhere—and most of them are being built by trial and error. Agent Design Patterns distills the experience of a growing community of agent builders into 20+ composable, reusable patterns for building AI agents that are reliable, efficient, controllable, and easy to reason about. In this uniquely valuable book, author and NVIDIA Research scientist Peter Belcak gives you a durable engineering vocabulary and design intuition that outlasts any new model release.

Peter Belcak

Agent Design Patterns by Peter Belcak is about moving beyond one-off prompts and treating agents as systems that can be designed, tested, and improved. It introduces reusable patterns for common challenges in agent development, particularly around capability, reliability, and robustness.

The book takes a practical approach. Peter builds a mortgage advisor to demonstrate how the patterns work together, then explores individual patterns such as:

• Errand, for breaking complex workflows into manageable units
• Guardrail, for gating actions and improving reliability
• Resampler, for using repeated sampling to produce more dependable results

Five chapters are currently available through Manning’s Early Access Program. Readers can follow the book as Peter writes it, receive new chapters when they’re released, and send feedback that may help shape the finished book.

If you’re already experimenting with agents, I’d be interested to hear which design problems have caused you the most trouble. For me, the gap between an impressive demo and an agent you can actually depend on is where this topic gets particularly useful.


Don’t forget you can get 45% off with your Devtalk discount! Just use the coupon code “devtalk.com” at checkout :+1:

First Post!

mira_tokarev

mira_tokarev

The distinction between making an agent capable and making it dependable feels important. Guardrails help with the obvious unsafe actions, but recovery after a partial tool failure is where many systems still become brittle. I’d be interested in seeing the patterns tested under interrupted runs: can the agent resume from a checkpoint without repeating side effects, and can it explain why it chose a fallback? Resampling seems useful for ambiguous decisions, but I’d probably reserve it for the steps where uncertainty is measurable rather than making every workflow more expensive.

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