AI Agents for Leaders

Name: AI Agents for Leaders
Rating: 4.5 (88 reviews)

Understand, Lead, and Strategically Apply AI Agents Across Industries — Without Writing Code

Role Play

Created byProf. Dr. Ana-Maria Olteteanu

Last updated 1/2026

English

English [Auto],

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.

Course content

5 sections • 19 lectures • 2h 53m total length

From RPA Bots to Agents - What makes agents different12:10
Explore how AI agents differ from rule-based bots and RPAs, combining perception, memory, planning, and autonomous decision making to adapt, learn, and use tools in healthcare.
What Can AI Agents Do - A Tour of Examples: Healthcare Agents, Enterprise Agents7:15
Explore how ai agents augment human capability across healthcare and enterprise, from diagnostic copilot and care coordination to email triage and meeting prep, including multimodal interaction.
What Can AI Agents Do - Examples Tour: Productivity Agents, Multimodal Agents7:50
Explore how planning agent, habit tracker agent, and life coach agent empower personal life management, plus multimodal agents in medicine, smart homes, and cross-channel support.

Core Components of Modern AI Agents5:08
Define how perception, memory, decision making, and action shape modern AI agents, using tokenization, embeddings, feature extraction, and memory with vector databases to connect inputs with past knowledge.
Tool Use: How Agents leverage External Capabilities10:56
AI agents use external tools to retrieve real-time data, manipulate software, and make dynamic decisions—via search, calculation, APIs and plugins, and code execution—while addressing latency, safety, and transparency.
Memory and Retrieval: How Agents Remember and Learn9:51
Explore how AI agents use memory: short term, episodic, and long term—guided by vector databases and retrieval augmented memory to recall, learn, and personalize interactions while addressing privacy.
Planning and Reasoning in AI Agents8:42
Explore planning and reasoning in ai agents, from chain of thought and multi-step planning to task decomposition and planning graphs, to enable coherent, self-correcting decision making.
Recursive and Iterative Learning in AI Agents11:39
Explore recursive and iterative prompting that lets AI agents reflect, self-correct, and refine outputs through multi-pass reasoning and prompt chaining for high-stakes tasks.
Capability Design Quiz: Choosing the Right Agent Approach

Learning Mechanisms in Advanced AI Agents8:23
Explore learning mechanisms that let advanced AI agents evolve across sessions and tasks. Learn few-shot and in-context learning, feedback loops, and reward-based adaptation to improve over time.
Interactive Communication in Agents: Dialogue and Task Negotiation7:35
Learn how ai agents coordinate intentions, negotiate goals, and collaborate with humans and other agents to achieve shared goals through turn taking and message passing.
Taking Action – Autonomous Behavior Across Contexts6:49
Explore how ai agents act autonomously—sending emails, updating calendars, or placing orders via APIs—while safety layers, human-in-the-loop checks, and permissions ensure responsible outcomes.
Taking Action II - Agents with Embodiment9:34
Explore how embodied agents translate digital intelligence into physical and virtual actions. Learn examples from surgical robots, warehouse automation, avatars in VR/AR, and metaverse companions, plus safety and trust challenges.
Advanced Capabilities Quiz: Communication, Learning & Action

Design Patterns and Architectures for Agents11:12
Identify modular components of modern AI agents and compare frameworks like Autogen, crew I, and Land Graph to design scalable, reliable agent architectures.
Implementing a Simple AI Agent Step-by-Step6:57
Learn to build a simple AI agent from goal definition to testing, integrating tools and memory, and choosing architectures to enable reliable, proactive agents.
Implementing a Customer Service escalation agent8:45
Design and implement a customer service escalation agent by mapping intent to architecture, using Cry or Lang graph to analyze tone, check history, and generate replies.
From AI Research Assistants to Coaching and Support Agents13:13
Explore modular ai agents for leadership, from web-searching research assistants that summarize content to meeting assistants that extract decisions and emails, and reflective coaching agents that support leadership reflection.
Pitching an AI Agent Solution to Your Leadership Team

Multiagent AI systems in practice8:34
Master how multiagent systems achieve scalability and modularity by coordinating specialized agents through collaboration, delegation, and structured communication, supported by diverse frameworks and no-code tools.
Active Areas of Research in AI Agents8:41
Explore active research in artificial intelligence agents across planning, memory systems, reward modeling, robustness, and safety layers like planning guards to enable dynamic replanning.
Trends and the Future of AI Agents10:39
Explore trends in personal and decentralized AI agents, including persistent memory and open-ended autonomy, and learn how leaders design modular, ethically governed systems.

Requirements

None

Description

AI agents are transforming work—and leaders need to understand how to harness them.

This course is designed for professionals, innovators, and decision-makers who want to lead confidently in the age of AI agents. You’ll gain a clear, practical understanding of what agents are, how they work, and how they’re being applied across sectors like healthcare, enterprise, and education—without needing to write code.

In this course we will explore:

What makes AI agents different from bots or automation scripts
Core capabilities: memory, planning, tool use, communication, and learning
Use cases: from care coordination and workflow agents to productivity and coaching companions
How to evaluate frameworks like LangChain, CrewAI, and LangGraph (without needing to build them yourself)
Leadership insights on agent architecture, team readiness, safety, and ethical design
Future trends: multi-agent systems, embodied agents, and strategic AI roadmaps

You’ll leave with the ability to speak the language of AI agents, evaluate tools, identify valuable use cases, and guide technical or business teams in responsible deployment.

Who This Course Is For:

Innovation and digital transformation leaders
Product and strategy professionals exploring AI capabilities
Business professionals who want to lead AI adoption — not just watch it happen
Consultants and team leads who need to bridge business and AI development teams
AI professionals, data scientists and programmers wanting to understand the principles of AI agents

No coding skills required. Just a sharp mind, a curiosity for emerging technologies, and a desire to shape the future of work.

Who this course is for:

Business Leaders looking to understand what can be achieved with AI an agents
Industry leaders looking for inspiration and developing thought leadership within their industry
Software professionals looking to get into AI agents and agentic architectures
Professionals and technically curious individuals who want to understand, build, and apply AI agents in real-world context
Data science professionals
Project managers
Innovation professionals
Consultants
Students
Business owners looking to add Agentic AI to their business
Entrepreneurs looking to build their next venture in the AI agentic space
Beginner Python developers curious about AI Agents
Digital transformation professionals who want to leverage AI agents to improve workflows, productivity, or decision-making in their organizations.
Product managers and innovation leads exploring AI-driven product features or planning AI agent integrations in existing tools.
AI-curious developers or data professionals who want to go beyond prompt engineering to architect and implement real AI agent systems.
Healthcare, education, or enterprise leaders seeking to understand how autonomous or semi-autonomous agents can be safely and ethically applied in domain-specific contexts.
Academic or applied researchers looking for a structured, application-driven introduction to the current state of agent technology.

AI Agents for Leaders

What you'll learn

Explore related topics

Course content

Foundations of AI Agents3 lectures • 27min

AI Agent Capabilities and Techniques5 lectures • 46min

Advanced AI agent capabilities and techniques4 lectures • 32min

Building Your Own Agent5 lectures • 40min

Advanced AI Agent topics, Agents in the Future3 lectures • 28min

Requirements

Description

Who this course is for: