5-Day AI Agents Intensive Learn Guide

Key Learnings from the AI Agents Intensive Course
Day 1 - Prompt Engineering Depth: Beyond simple "prompt crafting," I learned about systematic approaches like Chain-of-Thought, Tree-of-Thought, and Constitutional AI prompting. The evolution from GPT-3's completion style to modern instruction-tuned models fundamentally changed how I think about LLM interaction.
Day 2 - Vector Databases in Practice: The tradeoffs between different similarity search algorithms (HNSW vs. IVF) and embedding models (dense vs. sparse) were eye-opening. The concept that "similarity" is itself a design choice depending on the domain was particularly valuable.
Day 3 - Agent Architecture Patterns: The breakdown of agents into components (planning, memory, tools, reflection) and the development cycle of "plan-execute-evaluate-refine" provided a concrete framework. The recent "agent space" development showing how agents can collaborate or compete was fascinating.
Day 4 - Specialization Strategies: Learning about domain-specific LLMs revealed that successful specialization isn't just about more domain data, but about curated data, specialized tokenization, and task-specific fine-tuning approaches.
Day 5 - MLOps Evolution: The concept that Generative AI requires extending traditional MLOps with new concerns: prompt versioning, embedding drift detection, and different cost/performance tradeoffs.
** Evolution of My Understanding**
From Tools to Systems: Before the course, I viewed AI agents as sophisticated chatbots. Now I understand them as reasoning systems with iterative problem-solving capabilities, memory persistence, and tool orchestration.
From Monolithic to Component-Based: I previously thought of agents as single entities. The course revealed they're better understood as composed systems where each component (retrieval, reasoning, execution) can be independently optimized and evaluated.
The Importance of Evaluation Evolution: I gained appreciation for how agent evaluation differs from model evaluation - requiring task-based benchmarks, robustness testing, and safety assessments that account for autonomous tool use.
Practical Tradeoffs: The course transformed my abstract understanding into practical decision-making knowledge: when to use RAG vs. fine-tuning, when to build multiple specialized agents vs. one general agent, and how to balance latency, cost, and accuracy.
Production Realities: I now see AI agents not as research projects but as production systems requiring monitoring, versioning, and maintenance strategies adapted from but extending traditional MLOps.
Most Valuable Insight
The overarching theme was "intelligence through architecture" - that sophisticated AI behavior emerges not just from larger models, but from thoughtful system design that combines models with memory, tools, and iterative processes. The most powerful agents seem to be those with the most constrained, well-defined interaction patterns rather than those with the most capabilities. This course fundamentally shifted my perspective from focusing solely on model capabilities to considering the entire system architecture as the unit of "intelligence."