AI Agents Crash Course (2 Weeks)

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

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

Crash Course Outcomes

By the end of this crash course, learners should be able to:

• 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

Week 1: Build the Core Agent

• 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 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.

Week 2: Memory, Evaluation, and Safety

• 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
  • 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.