Peter Belcak

Peter Belcak is an AI Research Scientist, currently at NVIDIA Research. Prior to that he was at Meta GenAI and ETH Zurich. He has contributed across the entire AI stack, from AI efficiency, through efficient model training and tuning frameworks to NVIDIA’s Enterprise AI agent frameworks and products. He currently researches methods for improving the reliability of agentic systems and the formation of logically correct reasoning systems, and leads product efforts on LLM hallucination elimination and LLM advertising.

books by Peter Belcak

Agent Engineering

  • October 2026
  • ISBN 9781638359265
  • 105 pages

Build agents you can understand, test, and improve! This practical four-chapter ebook shows you how to choose the right level of autonomy, connect agents to useful tools, break complex processes into manageable parts, and catch weak spots with evaluation and feedback. By the end, you’ll have a solid foundation for creating more reliable agent systems and the technical judgment to take on more advanced agent development.

Agent Design Patterns

  • MEAP began August 2026
  • Last updated September 2026
  • Publication in Spring 2027 (estimated)
  • ISBN 9781633434318
  • 325 pages (estimated)
  • printed in black & white
resources: Book forum

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.

Each pattern included here is production tested and designed to click together as you construct transparent, testable, deployment-ready agentic applications. You’ll appreciate the familiar presentation style, with a simple template of a named problem, a clear solution, benefits, drawbacks, popular variants, and rules for composition with other patterns. Informative diagrams, worked examples, and Belcak’s clear mathematician-turned-engineer voice make even the most subtle patterns easy to internalize.

By the end, you’ll be able to read an underperforming agent, pinpoint the exact quality that is lacking, and reach for the specific pattern that addresses it. You’ll lower per-run costs without redesigning your agent, and even build self-improving agents that auto-tune their own prompts.