Part 5: AI agents
This final part brings everything together, taking you from structured workflows to fully capable agents that can think, decide, and act. You’ll move beyond static flows and build dynamic, tool-using agents in LangGraph—systems that can choose which tools to call, interpret results, and adapt their next steps based on context. From there, you’ll scale up to multi-agent systems that coordinate across specialized roles, routing tasks intelligently and collaborating to solve complex problems.
You’ll also connect your agents to the broader AI ecosystem through the Model Context Protocol (MCP), which allows them to discover and use remote tools as seamlessly as local ones. With MCP, your agents gain access to a growing world of interoperable services without extra integration overhead. Finally, you’ll learn how to make your agents production-ready by adding memory for long-term context, guardrails for safety, and observability for traceability—all while ensuring your system remains resilient and easy to debug.
The unifying theme here is practicality: building agents that aren’t just smart, but also reliable, transparent, and maintainable. By the end of this part, you’ll understand how to design agents that can reason effectively, collaborate efficiently, and operate safely in real-world environments—ready to grow from a single assistant into an entire ecosystem of intelligent, connected AI services.