Agent Engineering Stack: From Zero to Production in 3 Days

- How to build agents using the ReACT pattern (Reason, Act, Observe) - Request-response agents with Claude Agent SDK and OpenAI Agents SDK - RAG implementation with FAISS and pgvector for retrieval - Building your own minimal agent framework (~140 lines of code) - Multi-agent coordination patterns (sequential, parallel, hierarchical) - Framework comparison: Autogen, LangGraph, OpenAI Swarm - When to use agents vs. simple prompts and common pitfalls

You'll learn to build prompts and agentic applications with Python, master context engineering techniques, and develop advanced AI integration skills for technical applications.

- Write effective system prompts for regularized outputs or tool use - Design and implement agents capable of using, creating, and managing tools - Develop agents with autonomous action capabilities, including scheduling and event-triggered responses - Utilize open-source tool hubs designed for Large Language Models - Manage and economically host large vector stores - Construct self-improving agents that can evolve their prompts - Create and manage swarms of agents collaborating on complex goals - Design meta-swarms and information hierarchies for advanced agent collaboration and secrecy - Evaluate and create benchmarks for LLM performance analysis