This three-course specialization guides AI practitioners and developers through the complete journey of building practical AI agent systems — from single-agent architecture to multi-agent collaboration to production deployment. You will master modular agent design using LangGraph for graph-based workflows, Pydantic-AI for structured validation, and Mem0 for persistent memory, building agents that perceive, reason, and act across real-world scenarios.

As you progress, you will design multi-agent collaboration systems using CrewAI and Agno — defining planner, executor, reviewer, and critic roles with shared memory and communication patterns. The final course brings everything together by integrating LLMs from OpenAI and Anthropic into orchestrated workflows, adding production-ready state management, deploying via FastAPI, and implementing monitoring and evaluation pipelines. By the end, you will be able to architect, coordinate, and deploy multi-agent systems integrated with external APIs for enterprise automation.

应用的学习项目

Throughout the specialization, learners complete applied labs, agent-building projects, and end-to-end deployment exercises. You will build an "Email-to-Task" agent using LangGraph with structured I/O via Pydantic models, add persistent memory using Mem0, and create a Research Assistant Agent that reads articles, summarizes, and saves key facts for recall.

In the multi-agent course, learners build a "Content Team" using CrewAI with Researcher, Writer, and Editor roles, implement shared memory hand-offs, and design a Customer Support Workflow that triages and resolves tickets autonomously. The final project challenges learners to build a "Business Workflow Assistant" with plan-execute-report capabilities, deploy it using FastAPI and Agno runtime, and integrate with external APIs including Slack, Notion, and CRM systems — producing a production-grade multi-agent orchestration system with monitoring, evaluation pipelines, and structured safety validation.