Module 1
Understanding AI Agents
Build a clear mental model for what makes an AI agent different from a chatbot, script, or workflow automation tool.
Lesson 1
What Makes an Agent an AgentDefine autonomy, goals, tools, and feedback loops in plain language.Lesson 2
Agents vs. Assistants vs. AutomationsCompare reactive chat experiences with proactive, task-oriented systems.Lesson 3
The Core Agent LoopLearn the observe, reason, act, and reflect cycle used in most agent frameworks.Lesson 4
Where Agents Create Business ValueReview common use cases across support, research, operations, and internal productivity.Module 2
LLM Basics for Agent Builders
Understand the model capabilities and constraints that shape every agent system.
Lesson 1
How Large Language Models WorkCover tokens, next-token prediction, context windows, and inference behavior.Lesson 2
Strengths and Failure ModesIdentify where LLMs excel and where they hallucinate, drift, or overconfidently guess.Lesson 3
Prompting for Reliable OutputsUse role, task, context, and output constraints to improve consistency.Lesson 4
Choosing the Right ModelMatch model speed, cost, and capability to beginner agent use cases.Module 3
Tool Use and Action Taking
Give agents safe, structured access to APIs, files, and external services.
Lesson 1
Why Tools MatterSee how tool calling extends a model beyond static text generation.Lesson 2
Designing Clear Tool SchemasWrite tool descriptions and inputs that reduce ambiguity and bad calls.Lesson 3
Validating and Routing Tool ResultsHandle success, failure, retries, and partial outputs in an agent loop.Lesson 4
Safe Execution PatternsAdd human approval, rate limits, and guardrails before tools touch real systems.Module 4
Memory Systems and Context
Help agents remember the right information without losing control of relevance or cost.
Lesson 1
Short-Term vs. Long-Term MemorySeparate conversation state from durable knowledge and user history.Lesson 2
Working with Session ContextManage prompts, state objects, and summaries across multi-step tasks.Lesson 3
Storing Useful MemoriesDecide what information to save, ignore, compress, or expire.Lesson 4
When Memory Hurts PerformanceAvoid context bloat, stale data, and retrieval patterns that confuse the model.Module 5
Building Your First Agent
Assemble a complete beginner-friendly agent workflow from prompt to output.
Lesson 1
Scoping a First Agent ProjectChoose a narrow, testable use case that can ship in days instead of weeks.Lesson 2
Defining Inputs, Outputs, and SuccessTurn vague agent ideas into measurable workflows with clear boundaries.Lesson 3
Implementing the Agent LoopConnect prompts, tools, memory, and execution steps into one flow.Lesson 4
Testing with Real ScenariosRun happy-path and edge-case examples to find weak prompts and logic gaps.Module 6
Deploying and Improving Agents
Move from local prototype to production-ready agent service with basic monitoring and iteration.
Lesson 1
Packaging an Agent for DeploymentPrepare environment variables, configuration, and runtime dependencies.Lesson 2
Logging and Basic ObservabilityTrack prompts, tool calls, latency, and failures for debugging.Lesson 3
Managing Cost and ReliabilityUse caching, model selection, and fail-safes to keep beginner deployments stable.Lesson 4
Iteration RoadmapPlan the next versions of your agent based on feedback and usage data.