Master the core reasoning architectures—Reflection, Tool Use, Planning, and Multi-Agent Collaboration—that transform LLMs into autonomous problem solvers.

The Four Design Patterns of Agentic Workflows

Evaluate the architectural trade-offs between leading frameworks to choose the right environment for stateful, multi-agent orchestration.

Orchestrating Agents: Comparing LangGraph, CrewAI, and AutoGen

Learn to implement short-term state persistence and long-term RAG-based memory to ensure agents maintain context during long-horizon task execution.

Managing State and Memory in Persistent Agents

Develop robust evaluation pipelines using reasoning traces, tool-call benchmarks, and synthetic datasets to ensure reliability in non-deterministic workflows.

Testing and Evaluating Agentic Performance for Production

Shift from simple prompt-response loops to iterative AI systems that autonomously plan, execute tools, and self-correct. Learn to architect multi-agent systems that solve complex, non-linear tasks with minimal human intervention.