Hands-on Agentic AI: Building Intelligent Agents

The concepts of large language models (LLMs) took the world by storm in November 2022, positioning Artificial Intelligence as one of the most invested-in and promising technology sectors. This guided project will walk you through the creation of a reasoning and acting (ReAct) agent that harnesses the capability of the most prominent LLM in the world, GPT-4, to automate complex tasks that would normally require human reasoning and input. Ever wanted to know how to use large language models to interact with your business infrastructure or automate customer chat queries? This project is for you. By the end of this guided, ~1-hr long project, you will have created a GPT Assistant in Node/Typescript, that is able to answer questions on real-time information, such as the stock prices, and also answer questions on given input files. You will also understand the fundamentals of creating assistants that you can use and scale for your own business considerations. We will walk through the process from the beginning, from setting up your environment and API key, to uploading files and testing the limitations of retrieving relevant information, and creating functions that have reliable logic that can be scaled and changed depending on business need. This project, of intermediate complexity, is intended for those with some background in programming and application development to fully understand the logic and setup, though even business owners and managerial professionals can benefit from the project as we walk through every step and explain the cost-benefit analysis. Ready to create your own Assistant? Let's go!

Design and Govern Advanced Multi-Agent AI Systems is an intermediate-level course for AI engineers, data scientists, and technical leaders who need to architect collaborative AI systems that work reliably at scale. As the agentic AI market explodes with 56.1% growth, organizations are moving beyond single-agent implementations toward sophisticated multi-agent orchestration. This course equips you with the architectural thinking, governance frameworks, and practical implementation skills needed to design systems where multiple specialized agents collaborate effectively while maintaining safety and ethical standards. Through expert-led videos, real-world case studies from organizations like Anthropic and IBM, and hands-on labs with industry frameworks like CrewAI and LangGraph, you'll learn to architect agent networks, design communication protocols, and implement governance systems that scale. Whether you're building research assistants, customer service systems, or complex decision-making platforms, this course provides the frameworks and tools to create multi-agent systems that are greater than the sum of their parts.

This course explores the design and governance aspects of multi-agent AI systems - autonomous agents that collaborate, compete, and coordinate to achieve complex goals. Learners will gain a deep understanding of how to design, build, and govern multi-agent ecosystems, from defining core agent capabilities to orchestrating interactions at scale. The course emphasizes real-world applications, exploring how leading companies like LinkedIn, Anthropic, and Amazon deploy agentic AI to solve enterprise problems. Learners will explore the principles of coordination, communication protocols, and governance models, along with ethical and regulatory considerations for safe deployment. This course is ideal for AI enthusiasts, software developers, data scientists, and product managers who want to understand how multi-agent systems work in real-world environments. It’s also valuable for professionals working on AI governance, system design, or scalable automation projects. Learners should have a basic understanding of AI concepts and general computer science principles. No advanced AI or governance experience is required, making this course accessible to anyone eager to explore multi-agent systems and their design. By the end of the course, learners will have a practical foundation to design multi-agent workflows, evaluate performance trade-offs, and implement governance strategies that ensure responsible and efficient agent collaboration in business and research environments.

Building AI Agents for Complex Tasks is an intermediate-level course designed to equip learners with the skills to design, build, and evaluate intelligent agents that operate autonomously across dynamic, multi-step environments. Moving beyond simple chatbot flows, this course introduces learners to agent architectures that perceive context, make decisions, integrate tools, and recover from failure. Through hands-on labs, interactive video walkthroughs, and real-world case studies—including Alexa, BabyAGI, and AlphaCode—learners will explore agent types, design patterns, tool orchestration, memory management, and behavior evaluation. They'll gain practical experience using modern frameworks like LangChain and Rasa to construct agents for use cases such as research automation, virtual assistants, and decision-making bots. By the end of the course, learners will have built and tested their own intelligent agent and developed the foundational skills to implement agent-based AI systems that can adapt, reason, and act in real-world applications.

Ethical Governance & Risk in Agentic AI is an intermediate-level course designed for professionals who need to navigate the complex landscape of autonomous AI systems while ensuring ethical compliance and responsible deployment. As AI systems become increasingly autonomous, traditional governance approaches break down, creating new categories of risk and accountability challenges. This course equips learners with practical frameworks to assess AI autonomy levels, design adaptive compliance strategies, and build scalable governance structures. Through real-world case studies from IBM, UC Berkeley, and Harvard Business Review, learners explore both the opportunities and risks of agentic AI systems. The course emphasizes practical implementation, providing tools and templates that can be immediately applied in organizational settings. By the end, learners will be able to build comprehensive governance frameworks that balance innovation with responsibility, ensuring AI systems serve human values while driving competitive advantage.

Navigating Multi-Agent Communication Protocols is an intermediate-level course designed for AI engineers and system architects who need to build sophisticated multi-agent systems where effective communication and coordination are critical. In today's AI landscape, isolated agents are obsolete—success depends on seamless collaboration between multiple intelligent agents working toward shared objectives. This course provides comprehensive coverage of three essential communication protocols: Multi-Agent Communication Protocol (MCP) for standardized communication, Agent-to-Agent (A2A) for dynamic task coordination, and Agent Collaboration Protocol (ACP) for complex workflow orchestration. Through real-world case studies from organizations like Anthropic, Google, and IBM, hands-on implementation exercises, and practical design challenges, you'll learn to strategically select and integrate these protocols to solve complex coordination problems. Whether you're building autonomous systems, enterprise AI solutions, or collaborative AI applications, this course equips you with the knowledge and skills to transform chaotic agent interactions into orchestrated, efficient collaborations that deliver measurable business value.

LangGraph Framework is an intermediate-level course designed for developers and AI engineers who want to build production-ready, stateful AI systems that go beyond simple prompt-response interactions. In today's AI landscape, the most powerful applications aren't single agents working in isolation—they're coordinated systems that maintain context, make intelligent decisions, and collaborate to solve complex problems. This course teaches you to harness LangGraph's graph-based architecture to create AI workflows with persistent memory, conditional logic, and multi-agent coordination. Through hands-on labs, real-world case studies from companies like Klarna, CyberArk, and Replit, and practical projects, you'll learn to build systems that maintain context across interactions, handle failures gracefully, and coordinate multiple specialized agents to create emergent intelligence. Whether you're building customer service automation, research assistants, or complex business workflows, this course equips you with the skills to create AI systems that are not just intelligent, but reliable, maintainable, and production-ready.