I’m trying to learn how one can build agentic AI systems similar to Claude Code, and eventually adapt that knowledge toward domain-specific use cases (e.g., “Claude Code for healthcare, finance, education, etc.”).

For those of you who’ve studied or built these kinds of systems, I’d love to hear your recommendations on:

• Foundational learning: What books, courses, or papers provide the best grounding for understanding LLM-based systems and their decision-making?

• Architectural patterns: What design patterns are worth studying for things like context management, memory, reasoning, and orchestration?

• Build vs. deploy: How do you think about building internal systems vs. packaging/distributing them as APIs, SDKs, or products?

• Open source projects: Which ones are most valuable to study for internals (decision making, evals, context engineering, tool use, etc.)?

• Evals and observability: What tools or products help evaluate quality, measure system behavior, and observe performance in real-world use?

• Models: Which models are best suited for “thinking” (reasoning, planning, decomposing problems) vs. “doing” (execution, coding, retrieval)?

• Learning path: How would you approach going from theory → prototype → production-quality system?

My goal is to discover high-quality resources that one can truly spend time learning from and building with—through iteration and practice—while also sharing what I learn so others on the same path can benefit.

Thanks in advance for sharing your experiences and guidance!