AI Patterns consolidates common problem/solution pairs — patterns — for machine, deep, reinforcement, and generative AI, distilled from hundreds of customer, partner, and individual projects. The goal is practical: give practitioners a shared vocabulary and reusable building blocks for moving AI systems from experiments to production.
The catalog spans two decades of architecture practice — from classical machine-learning and data-science patterns through retrieval and reasoning, to the recent body of work on agentic and multi-agent systems. The agentic patterns draw on Agentic Architectural Patterns for Building Multi-Agent Systems (Dr. Ali Arsanjani & Juan Pablo Bustos, Packt, 2026).
Where to start
- Start Here — Reading Guide: a guided path through the whole series, organized by the question you’re bringing.
- Pattern Catalog Summary: an interactive navigator for all 56 agentic patterns — search and filter by type or maturity level.
- AI Patterns: the full directory of every article and reference page, grouped by topic.
- Blog: everything in reverse-chronological order.
Contact
Have you used one of these patterns, or a variation? Comments and experience reports are welcome — get in touch. Curated by DeepContext LLC.
Disclaimer
The patterns and best practices on this site have been mined from real projects and tested in actual engagements. Even so, their applicability to your particular project, context, or requirements is not guaranteed and not suggested, and results will vary. This material is provided for general informational purposes only and does not constitute professional, legal, financial, or engineering advice. Any use you make of these patterns, practices, code, or recommendations is entirely at your own risk and your own responsibility, including the responsibility to independently evaluate, test, and validate suitability for your use case. The content is provided “as is,” in good faith and in the hope that it will help you, without warranties of any kind, express or implied, including fitness for a particular purpose; DeepContext LLC and the authors accept no liability for any loss or damage arising from its use.