Agentic AI Made Simple Course

Lesson 1 • Agents vs. Workflows vs. Copilots

Clarifies terminology confusion that derails real projects. Provides a decision framework for choosing the right paradigm for a given task.

Lesson 2 • Business Value and Use-Case Landscape

Maps agentic AI to concrete productivity and revenue outcomes. Prepares students to identify high-value opportunities in their own organizations.

Lesson 3 • The Agent Loop Explained

Breaks down the observe-think-act cycle that drives every agentic system. Students map this loop onto a concrete example to solidify understanding.

Lesson 4 • Core Properties of an AI Agent

Defines perception, memory, planning, and action as the four pillars of agency. Connects each pillar to real agent behavior students will build later.

Lesson 1 • Reasoning Patterns Inside LLMs

Covers chain-of-thought, step-back prompting, and self-reflection as reasoning strategies. Students apply each pattern to a sample agent task.

Lesson 2 • How LLMs Generate Decisions

Explains token prediction, context windows, and temperature as the mechanics behind agent reasoning. Grounds later prompt engineering in accurate mental models.

Lesson 3 • Common LLM Failure Modes

Catalogs hallucination, sycophancy, and context drift as the top risks in agentic pipelines. Knowing failure modes enables proactive mitigation design.

Lesson 4 • Prompt Engineering for Agentic Tasks

Teaches system prompt design, role assignment, and output formatting for reliable agent behavior. Directly enables the tool-use and planning chapters that follow.

Lesson 1 • Tool Use and Function Calling

Explains how agents invoke external functions, APIs, and services to act on the world. Establishes the tool-use pattern used throughout all later agent builds.

Lesson 2 • Agent State and Context Management

Addresses how agents track progress, maintain state across steps, and avoid context overflow. Prepares students for multi-step agent builds in Chapter 4.

Lesson 3 • Planning and Task Decomposition

Teaches ReAct, plan-and-execute, and hierarchical planning patterns. Students decompose a complex goal into an executable agent plan.

Lesson 4 • Retrieval-Augmented Generation in Agents

Covers embedding, chunking, and retrieval pipelines that ground agents in accurate knowledge. Directly reduces hallucination risk identified in Chapter 2.

Lesson 1 • Designing the Agent Before Coding

Introduces a design-first methodology covering goal definition, tool inventory, and success criteria. Prevents the common mistake of coding before clarifying requirements.

Lesson 2 • Implementing the Core Agent Loop

Guides students through coding the observe-think-act loop with tool integration. Produces the reusable agent scaffold used in all subsequent chapters.

Lesson 3 • Setting Up the Development Environment

Walks through environment configuration, API key management, and dependency setup. Ensures every student has a reproducible workspace for all hands-on labs.

Lesson 4 • Testing and Iterating on the Agent

Covers manual testing, edge-case probing, and prompt iteration to improve reliability. Builds the quality mindset carried into advanced agent design chapters.

Lesson 1 • Why Single Agents Have Limits

Analyzes context overflow, skill breadth, and reliability ceilings that motivate multi-agent design. Sets the problem context before introducing solutions.

Lesson 2 • Debugging Multi-Agent Failures

Teaches trace-based debugging, message logging, and failure attribution in distributed agent systems. Prepares students for the reliability challenges of production deployments.

Lesson 3 • Inter-Agent Communication Protocols

Covers message passing, shared state stores, and event-driven triggers for agent coordination. Reliable communication is the foundation of stable multi-agent systems.

Lesson 4 • Building a Two-Agent Research Pipeline

Hands-on lab where students build a researcher agent and a writer agent that collaborate. Applies orchestration concepts to a realistic content-generation workflow.

Lesson 1 • Failure Modes Unique to Agentic Systems

Catalogs cascading errors, goal misalignment, and unintended side effects specific to autonomous agents. Understanding these risks motivates every safety technique in this chapter.

Lesson 2 • Building a Safety Review Checklist

Synthesizes chapter content into a reusable pre-deployment safety checklist. Students apply the checklist to evaluate their own agent builds.

Lesson 3 • Responsible AI Principles for Agents

Applies fairness, transparency, and accountability principles to agentic system design. Connects ethical frameworks to concrete implementation decisions.

Lesson 4 • Guardrails and Constraint Enforcement

Teaches input/output filtering, action whitelists, and scope constraints to bound agent behavior. Directly reduces the risk of agents taking harmful or unauthorized actions.

Lesson 1 • Production Readiness Assessment

Defines the gap between prototype and production across reliability, security, and observability dimensions. Students score their own agent against a readiness rubric.

Lesson 2 • Scaling and Cost Optimization

Addresses horizontal scaling, caching, and model routing strategies to control cost at scale. Balances performance requirements against operational budget constraints.

Lesson 3 • Containerization and Deployment Patterns

Covers containerizing agent services, environment variable management, and deployment pipeline basics. Provides a portable deployment approach independent of cloud provider.

Lesson 4 • Versioning and Continuous Improvement

Establishes prompt versioning, A/B evaluation, and feedback loops for ongoing agent improvement. Closes the loop between production signals and development iterations.

Lesson 1 • Measuring Agent Impact and ROI

Defines KPIs, measurement methodologies, and reporting cadences for agentic AI programs. Enables data-driven decisions about where to invest and where to pause.

Lesson 2 • Domain-Specific Agent Customization

Teaches fine-tuning, domain-specific tool libraries, and knowledge base curation for vertical applications. Students adapt a general agent to a domain of their choice.

Lesson 3 • Building an Organizational AI Agent Strategy

Provides a framework for prioritizing, governing, and scaling agent initiatives across business units. Translates technical capability into executive-level strategic planning.

Lesson 4 • Self-Improving and Reflective Agents

Covers agents that critique their own outputs, update their strategies, and learn from feedback. Represents the frontier of practical agentic capability available today.