AI Agent Manager

An AI Agent Manager designs and deploys intelligent agents that think, remember, and collaborate.

• 5 Hours Live Online Event (Two 2.5 hour sessions)
• 5 Hours of Self-Paced Practicum

The AI Agent Manager course is specifically designed to take you beyond basic AI automation and into the emerging world of intelligent agents. Through a hands-on approach, you’ll learn to build goal-oriented agents that can reason, remember, plan, and collaborate using leading open-source frameworks such as LangGraph, CAMEL, and ReAct.

As you progress, you’ll work through guided labs, system architecture design exercises, and a capstone deployment project. In doing so, you’ll gain practical experience creating AI agents with persistent memory, adaptive behavior, human-in-the-loop control, and secure deployment practices. Whether you’re designing executive assistants, task coordinators, or research bots, this course provides a clear blueprint for building real-time, production-grade AI systems.

By the end of the course, you’ll have designed and deployed intelligent agents with real-world utility. Consequently, you’ll be fully prepared to complete the AI Agent+ certification, which includes both the AI Automation Engineer and AI Agent Manager courses as part of the AI Agent Manager Program.

Prerequisites

• Completion of AI Automation Engineer (AI Auto+) or equivalent
• Familiarity with prompting, vector stores, and function-calling APIs
• Comfortable with logic flows, decision trees, or structured thinking

Target Audience

• Product managers and AI leads building advanced LLM-powered features
• Developers exploring LangGraph, CrewAI, and agent orchestration
• Startup teams deploying AI copilots or assistants
• Researchers or analysts needing multi-step memory-aware workflows
• Innovation and automation teams designing intelligent systems

Learning Objectives

By the end of this AI Agent Manager course, you will be able to:

• Design agentic architectures using frameworks like CAMEL and ReAct
• Implement agent memory using vector stores (e.g., FAISS, Pinecone)
• Build goal-based, reflexive, and planning agents
• Coordinate multi-agent systems with messaging and state sync
• Deploy agents to real environments (Slack, dashboards, or voice)
• Enforce agent boundaries using security, ethics, and human escalation mechanisms