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Introduction to Agentic AI

You’ll explore how agentic AI differs from traditional and generative AI, and examine the essential building blocks of agentic systems.

This course provides a practical introduction to autonomous AI systems that can pursue goals, make decisions, use tools, and perform multi-step tasks with varying levels of autonomy. Participants will explore how Agentic AI differs from traditional AI and generative AI while gaining an understanding of the core components of agentic systems, including models, goals, instructions, tools, memory, state management, and decision-making processes.

The course examines both single-agent and multi-agent architectures and demonstrates how they can be applied to solve complex business challenges. In addition, learners will develop an understanding of the foundational paradigms, architectural frameworks, and operational practices required to design, deploy, and manage AI agents. The course also covers the key aspects of operating AI agents in production environments, including testing, observability, AgentOps, security, governance, risk management, and human oversight.

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Who Should Attend?

This introductory-level course is geared for non-technical business professionals seeking to harness the power of Artificial General Intelligence (AGI) to improve their productivity, enhance decision-making, and drive innovation within their organizations. This course is valuable for a wide range of industries and job roles.

Some roles that might benefit from attending include:

  • Technical Professionals
  • AI Practitioners
  • Business Analysts
  • Software Developers & Architects
  • Technology Managers
  • Security, Governance and Compliance Professionals

What You'll Learn

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:

  • Distinguish Agentic AI from Other AI Paradigms: Define the core characteristics of agentic AI and differentiate autonomous agents from traditional machine learning models, generative AI applications, copilots, and deterministic automated workflows.
  • Architect and Deconstruct AI Agent Core Components: Identify and integrate the critical structural pillars of an agent—including foundation models, goal frameworks, system instructions, tools, memory systems, and state management.
  • Design Advanced Reasoning, Planning, and Prompting Mechanisms: Engineer prompts and behavioral instructions that guide autonomous planning and decision-making, enabling agents to decompose goals, use external tools, and execute complex, multi-step tasks.
  • Implement Multi-Agent Collaboration and Orchestration Patterns: Compare common agentic system architectures and design frameworks where multiple specialized agents collaborate, delegate tasks, and coordinate activities to solve distributed problems.
  • Deploy, Observe, and Evaluate Enterprise Solutions: Identify high-value enterprise use cases, determine appropriate levels of autonomy, and systematically evaluate performance using execution trajectories, tool interaction metrics, reliability, cost, and modern AgentOps observability practices.
  • Secure and Govern Autonomous Systems: Mitigate security risks—such as prompt injection, excessive permissions, and tool misuse—by applying robust guardrails, least-privilege access controls, human-in-the-loop interfaces, and responsible AI governance frameworks.

Course Outline

Module 1: Foundations of Agentic AI

  • Trace the evolution of AI agents.
  • Explore key agent types and behaviors.
  • Compare assistants, copilots, workflows, and agents.

Module 2: Introducing AI Agents

  • Explore core AI agent components.
  • Understand agent reasoning and execution.
  • Learn how agents use tools and data.

Module 3: Prompting for Agentic AI

  • Write prompts with clear goals and roles.
  • Guide planning, decisions, and tool use.
  • Add memory, constraints, and guardrails.

Module 4: Architectures and Patterns for Agentic Systems

  • Explore core agentic architecture components.
  • Compare workflows, agents, and common patterns.
  • Select architectures based on needs and risk.

Module 5: Tools, Decision-Making and Agent Behavior

  • Understand agent decisions and tool use.
  • Explore planning, execution, and evaluation.
  • Manage failures, loops, and autonomy.

Module 6: Multi-Agent Systems and Coordination

  • Explore multi-agent roles and collaboration.
  • Coordinate tasks, context, and results.
  • Evaluate benefits, failures, and complexity.

Module 7: Agentic AI in Real-World Applications

  • Explore enterprise agentic AI use cases.
  • Choose the right approach and autonomy.
  • Connect agents with systems, data, and people.

Module 8: Evaluating, Deploying and Operating AI Agents

  • Test and deploy AI agents.
  • Track performance, cost, and reliability.
  • Detect failures and continuously improve.

Module 9: Security, Governance and Responsible Agentic AI

  • Identify security and operational risks.
  • Apply guardrails, permissions, and oversight.
  • Address governance, privacy, and accountability.
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Prerequisites

This course doesn’t require a technical background, however in order to be successful in this course you need possess:

  • Basic computer literacy, and a fundamental understanding of business processes (such as marketing, data analysis, project management, or customer support, etc.).
  • Basic data manipulation skills: While not required, a basic understanding of working with data, such as using spreadsheets (e.g., Microsoft Excel or Google Sheets) for data organization and simple calculations, would be beneficial for participants.
  • An understanding of agentic AI concepts, architectures, and practical applications.
  • A general understanding of AI and Generative AI is helpful but not required.

Follow-On Courses

We offer a wide variety of follow-on courses and learning paths for Generative AI, AI for Business, Applied AI, Azure OpenAI, AI for developers, testers, data analytics, machine learning, deep learning, intelligent automation and many other related topics.