Course Outline

AI Agents Crash Course

Learn and understand practical AI agentic systems in an accelerated format. This course focuses on the essential concepts and implementation patterns needed to design, evaluate, and ship production-minded agentic systems.

Duration

4 Weeks

Outcome

Build, evaluate, and ship a prototype agent

What You Will Learn

Rather than covering every possible architecture, this crash course prioritizes the highest-leverage skills: agent fundamentals, tool use, memory, evaluation, safety, and deployment readiness.

Crash Course Outcomes

Distinguish AI agents from traditional prompt-based applications and workflows
Identify when agentic architectures are appropriate—and when simpler approaches are preferable
Build a tool-using single-agent workflow that can execute tasks reliably
Add short-term memory and retrieval-backed context to improve task performance
Evaluate agent behavior using task-based benchmarks and failure analysis
Implement basic safety guardrails, escalation paths, and tool execution controls
Deploy a prototype with observability, monitoring, and rollback considerations

Recommended Audience

Engineers who need a fast working understanding of agents
Product builders deciding whether an agent is the right architecture
Applied AI practitioners prototyping copilots or internal assistants

Weeks 1–2: Build the Core Agent

Core Build

Goals:

Understand when an agent is justified
Build a controllable tool-using workflow
Add stopping conditions and basic failure handling

Syllabus:

What agents are, when they are useful, and when simpler systems are better — AI Agents Overview
When simpler systems are better — When Not to Use an AI Agent
Why external tools expand what an agent can do — Tool-Integrated Reasoning
How broader ecosystem choices shape architecture decisions — Agentic Ecosystems
Core single-agent patterns and trade-offs — Single-Agent Architecture Patterns
Core multi-agent concepts — Multi-Agent Systems
Framework patterns for interactive agent applications — React Framework for Building AI Agents

Deliverable:

A working single-agent assistant that completes one narrow workflow with tools.

Weeks 3–4: Memory, Evaluation, and Safety

Ship Ready

Goals:

Add short-term state and optional retrieval-backed knowledge access
Measure task success and failure modes
Prevent unsafe tool execution and define escalation behavior

Syllabus:

Memory types, retention choices, and practical memory design — Memory in Agentic Systems
How to evaluate agentic systems — Agentic System Evaluation
How to harness agents for specific tasks — Agent Harness
Reusable capability packaging — Agent Skills
Agentic RAG: query planning, decomposition, iterative evidence gathering, stop conditions, and traceability — Agent RAG
Tool and context interoperability — MCP Model Context Protocol
How objective-driven coding assistants execute work — How GitHub Copilot Works in Agent Mode

Deliverable:

A prototype agent with a small eval set, guardrails, and a brief failure analysis.