AI Agents for Leaders
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Unclaimed ProfileThe rapid evolution of artificial intelligence has moved beyond simple chatbots to autonomous AI agents capable of planning, tool integration, and collaborative execution. AI Agents for Leaders, designed by Prof. Dr. Ana-Maria Olteteanu, provides a comprehensive, non-technical blueprint for business leaders, managers, and decision-makers looking to deploy these technologies strategically. Throughout this course, participants will learn to distinguish between traditional rule-based robotic process automation (RPA) and modern, intelligent agentic architectures. The curriculum breaks down the core components of agent systems, including memory, planning, autonomy, and interactivity. Leaders will gain critical insights into comparing leading open-source frameworks like LangGraph, AutoGen, and CrewAI, helping them determine which architecture best suits their specific organizational needs. Additionally, the course addresses real-world applications across key sectors such as healthcare, enterprise productivity, and education. By focusing on multi-agent workflows, tool integration, and crucial safety and ethical considerations, this program empowers leaders to design, evaluate, and direct agentic implementations without needing a background in coding, positioning their organizations at the forefront of the cognitive automation revolution.
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Prof. Dr. Ana-Maria Olteteanu
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Prof. Dr. Ana-Maria Olteteanu is an academic and expert at the intersection of Artificial Intelligence, cognitive science, and human-computer interaction. With a deep background in computational creativity and cognitive systems, she focuses on making AI accessible and deeply beneficial for human leadership and personal well-being. In her courses, "AI Agents for Leaders" and "AI for Happiness", she bridges the gap between complex algorithmic concepts and practical, human-centric applications. Her teaching philosophy emphasizes empirical understanding, ethical design, and actionable strategy, helping leaders and individuals leverage intelligent systems to automate workflows while enhancing human connection and mental flourishing. Through structured, evidence-based frameworks,…Show more
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Program Overview
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Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Distinguish between rule-based bots, RPA systems, and modern AI agents.
- Understand the foundations of Agents and Agentic Architectures
- Identify the main components of Agents and Agent Architectures
- Explore what makes an agent “intelligent,” including features like autonomy, memory, planning, and interactivity.
- Analyze state-of-the-art applications of agents in domains such as healthcare, enterprise, education, and personal productivity.
- Compare leading agent architectures (e.g., Autogen, CrewAI, LangGraph) and learn when to use each.
- Understand how to integrate tools, memory systems, and external APIs to enable agents to perceive, reason, and act in real or simulated environments.
- See how one can implement agents using open-source frameworks like LangChain, CrewAI, or LangGraph.
- Explore concepts such as recursive prompting, self-reflection, planning, negotiation, and agent collaboration.
- Design and simulate multi-agent workflows with communication, goal alignment, and safety considerations.
- Examine the ethical, technical, and societal challenges of autonomous agents—including embodiment, trust, and permission layers.
- Explore cutting-edge research directions and speculate on trends shaping the future of agent technologies.
- Design and prototype their own AI agents using open-source frameworks like LangChain, CrewAI, or LangGraph.
- Integrate agents into productivity, healthcare, or enterprise workflows with an awareness of memory, safety, planning, and tool use.
- Evaluate agent architectures and agent capabilities for internal deployments, product innovation, or research prototyping.
- Contribute to multi-agent systems or lead discussions about how AI agents will reshape industries.
Best For
- Business executives and managers aiming to integrate autonomous AI into operational workflows.
- Product leaders seeking to understand the architecture and strategic potential of agentic systems.
- Innovation consultants looking to advise clients on modern AI automation frameworks.
- Decision-makers who need to evaluate the feasibility and safety of multi-agent deployments.
Not For
- Software engineers looking for a deep-dive, hands-on coding tutorial on agent implementation.
- Absolute beginners who have no prior experience or conceptual understanding of AI or LLMs.
- Researchers seeking an exhaustive mathematical or algorithmic deep-dive into LLM weights and training.
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