š Exploring Agentic AI Systems š
Hello, LinkedIn community! Iām diving into the world of agentic AI systems, similar to Claude Code, with a vision to tailor them for specific areas like healthcare, finance, and education. Your insights could be invaluable on this journey!
š Key Areas of Interest:
- Foundational Learning: Recommendations for books, courses, or papers on LLM-based systems.
- Architectural Patterns: Design patterns for context management, memory, reasoning, and orchestration.
- Build vs. Deploy: Differentiating between internal systems and packaged solutions (APIs, SDKs, products).
- Open Source Projects: Valuable projects for decision-making, evaluations, and context engineering.
- Evals & Observability: Tools to assess quality and performance in real-world applications.
- Models: Choices for reasoning (“thinking”) vs. execution (“doing”).
- Learning Path: Steps to transition from theory to a production-quality system.
š” Let’s share our knowledge and grow together! Comment below with your thoughts, resources, and experiences. Your input can pave the way for innovation in AI!
