AI Builders and Learners SF

• ​Arize Phoenix GitHub: https://github.com/Arize-ai/phoenix
• ​Phoenix docs: https://docs.arize.com/phoenix

​​​Resources

• ​​​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​​​Events Calendar: https://luma.com/ai-builders-and-learners
• ​​​Slack (Discuss during the week): https://slack.flyte.org/
• ​​​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​​In this stream

• Intro to topic
• ​​​​Community Discussion
• Practical examples

​​​Community challenge (optional)
​​​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​​​Note on Flyte / Union
​​​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​​​Union: https://www.union.ai/
• ​​​Flyte: https://flyte.org/

​​​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Agentic Architectural Patterns for Building Multi-Agent Systems
Authors: Dr. Ali Arsanjani, Juan Pablo Bustos
Published: January 2026

Packt: https://www.packtpub.com/en-us/product/agentic-architectural-patterns-for-building-multi-agent-systems-9781806029563

O'rielly platform: https://learning.oreilly.com/library/view/agentic-architectural-patterns/9781806029570/

Chapters:
Part 1: Foundations and Core Agent Concepts
Chapter 1: GenAI in the Enterprise: Landscape, Maturity, and Agent Focus
Chapter 2: Agent-Ready LLMs: Selection, Deployment, and Adaptation
Chapter 3: The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning
Part 2: Agentic AI: Architecture and Design Patterns
Chapter 4: Agentic AI Architecture: Components and Interactions
Chapter 5: Multi-Agent Coordination Patterns
Chapter 6: Explainability and Compliance Agentic Patterns
Chapter 7: Robustness and Fault Tolerance Patterns
Chapter 8: Human-Agent Interaction Patterns
Chapter 9: Agent-Level Patterns
Chapter 10: System-Level Patterns for Production Readiness
Chapter 11: Advanced Adaptation: Building Agents That Learn
Part 3: Execution: Strategy, Use Cases, and The Future
Chapter 12: A Practical Roadmap: Implementing Agentic Patterns by Maturity Level
Chapter 13: Use Case: A Single Agent for Loan Processing
Chapter 14: Use Case: A Multi-Agent System for Loan Processing
Chapter 15: Agent Frameworks – Use Case: A Multi-Agent System for Loan Processing with CrewAI and LangGraph
Chapter 16: Conclusion: Charting Your Agentic AI Journey

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Book Description
Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs.
Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. You'll explore a rich library of agentic AI design patterns to address coordination, explainability, fault tolerance, and human-agent interaction. This book introduces a concrete, hierarchical multi-agent architecture where high-level orchestrator agents manage complex business workflows by delegating entire sub-processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol.
To ensure your systems are production-ready, we provide a practical framework for observability using life cycle callbacks, giving you the granular traceability needed for debugging, compliance, and cost management. Each pattern is backed by real-world scenarios and code examples using the open source Agent Development Kit (ADK).

Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group https://slack.flyte.org/

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About the book:
Title: RAG with Python Cookbook
Authors: Dominik Polzer
Published: May 2026

O'rielly platform: https://learning.oreilly.com/library/view/rag-with-python/9798341600553/

Chapters:

• 1. Getting Started with RAG
• 2. Foundation Models
• 3. Loading Data
• 4. Data Preparation
• 5. Embeddings
• 6. Vector Databases and Similarity Searches
• 7. Retrieval
• 8. Agentic RAG
• 9. Graph RAG
• 10. Evaluating RAG Systems
• 11. RAG Web Apps

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Book Description
As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.
Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.

• Learn core RAG components including embedding, retrieval, and generation techniques
• Understand advanced workflows like semantic-aware chunking and multi-query prompting
• Build custom solutions such as chatbots and autonomous agents for specific data challenges
• Continuously evaluate and optimize systems for accuracy, relevance, and performance