General familiarity with AI concepts is recommended. No prior experience building AI agents is required. Suitable for beginning to intermediate technical professionals, including those in software, web, cloud, platform, and related roles.

Course Outline:

Introduction to Agentic AI

1. Overview of agentic AI and how it differs from chatbots and prompt-based AI use
2. Understanding the shift from single-response AI tools to multi-step intelligent workflows
3. Common examples of AI agents in technical and business settings
4. How agentic AI is changing the design of modern AI-enabled systems
5. Capabilities and limitations of current agentic systems

Core Components of AI Agents

1. Instructions, goals, and task definition
2. Planning and task decomposition
3. Memory and context management
4. Tool use, data access, and external system interaction
5. Human-in-the-loop decision points
6. Outputs, actions, and workflow completion

Common Agent Design Patterns

1. Single-agent workflows
2. Prompt chaining and multi-step execution
3. Tool-calling patterns
4. Routing and task-selection approaches
5. Review, reflection, and self-check patterns
6. When to use an agentic approach versus a simpler AI workflow

Designing Effective AI Agents

1. Defining the task boundary and level of autonomy
2. Selecting the right inputs, outputs, and success criteria
3. Choosing tools and capabilities for the agent
4. Handling ambiguity, incomplete information, and failure scenarios
5. Designing predictable and useful agent behavior

Prompting and Orchestration Strategies

1. Writing effective instructions for AI agents
2. Structuring prompts for reasoning, action, and output consistency
3. Guiding decision-making and tool selection
4. Using constraints and follow-up prompting to improve behavior
5. Prompting for validation, checking, and refinement
6. Improving reliability through better orchestration design

Real-World Applications of AI Agents

1. Knowledge assistants and internal support agents
2. Research and analysis workflows
3. Documentation and content support
4. Workflow coordination and task assistance

Risks and Responsible Use

1. Identifying hallucinations and unreliable outputs
2. Managing tool access and permission boundaries
3. Protecting sensitive and proprietary information
4. Avoiding over-reliance on autonomous behavior
5. Knowing when human oversight is required

Training material provided: Yes (Digital format)