From Foundations to Production AI: November Highlights

From Foundations to Production AI: November Highlights

TLDR: The AI Agents Foundations series is now complete, providing a 9-part roadmap for developers to become AI engineers. The final lessons cover planning, building a ReAct agent from scratch, architecting memory, and creating multimodal agents that can "see".

Summary: This article provides a summary of the recently completed "AI Agents Foundations" series, a nine-part journey designed to equip Python developers with the skills to build and ship production-grade AI agents. The author recaps the final four lessons, which cover the crucial topics of planning, tool use, memory, and multimodality.

For architects and teams, this series offers a practical roadmap for moving beyond simple AI workflows to building true agentic systems. The article emphasizes the importance of understanding the core logic behind agents, rather than just relying on frameworks. It also teases a future article on the lessons learned from shipping vertical AI agents to production, highlighting the surprising finding that simpler architectures and domain-focused models often outperform complex RAG pipelines.

Key takeaways:

• The AI Agents Foundations series is a 9-part guide to becoming an AI engineer.
• The final lessons cover planning, building a ReAct agent, memory architecture, and multimodal agents.
• A guest post on LLM samplers explains how to control the quality and style of an LLM's output.
• A new series on AI Evals is coming soon, focusing on business-level metrics and LLM judges.

Link: November Highlights: From Foundations To Production AI