Teaching Agentic AI: Creating Workshops, Courses, and Content

Why Teaching Deepens Your Own Expertise

The best way to solidify your understanding of agentic AI is to teach it. Preparing a workshop forces you to identify the exact boundary between what you know and what you assume. Students ask questions you never considered, exposing gaps in your mental model that years of building would never reveal.

Beyond personal growth, teaching positions you as a domain expert. Engineers who create educational content get invited to speak at conferences, consult for teams adopting agent architectures, and build professional networks that accelerate their careers.

Designing Your Curriculum

Start with the end goal: what should a student be able to build after completing your course? Work backward from that outcome to define modules.

flowchart LR
    CALLER(["Student or Parent"])
    subgraph TEL["Telephony"]
        SIP["Twilio SIP and PSTN"]
    end
    subgraph BRAIN["Education AI Agent"]
        STT["Streaming STT<br/>Deepgram or Whisper"]
        NLU{"Intent and<br/>Entity Extraction"}
        TOOLS["Tool Calls"]
        TTS["Streaming TTS<br/>ElevenLabs or Rime"]
    end
    subgraph DATA["Live Data Plane"]
        CRM[("CRM and Notes")]
        CAL[("Calendar and<br/>Schedule")]
        KB[("Knowledge Base<br/>and Policies")]
    end
    subgraph OUT["Outcomes"]
        O1(["Enrollment captured"])
        O2(["Tour scheduled"])
        O3(["Counselor callback"])
    end
    CALLER --> SIP --> STT --> NLU
    NLU -->|Lookup| TOOLS
    TOOLS <--> CRM
    TOOLS <--> CAL
    TOOLS <--> KB
    NLU --> TTS --> SIP --> CALLER
    NLU -->|Resolved| O1
    NLU -->|Schedule| O2
    NLU -->|Escalate| O3
    style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a
    style NLU fill:#4f46e5,stroke:#4338ca,color:#fff
    style O1 fill:#059669,stroke:#047857,color:#fff
    style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff
    style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937

A proven structure for a full agentic AI course:

Module 1 — Foundations (2 sessions). LLM API basics, function calling, prompt engineering. Students build a single-tool agent from scratch using raw API calls.

Module 2 — Framework Mastery (3 sessions). Agent definition, tool registration, structured output, guardrails. Students rebuild their Module 1 agent using the OpenAI Agents SDK, then extend it with multiple tools.

Module 3 — Multi-Agent Systems (2 sessions). Handoffs, agent orchestration, conversation state management. Students build a three-agent system with routing logic.

Module 4 — Production (3 sessions). Tracing, evaluation, deployment, error handling, cost management. Students deploy their multi-agent system with full observability.

Building Hands-On Exercises

The most effective exercises follow the "scaffold and release" pattern: provide starter code that handles boilerplate, then ask students to implement the core logic.

# Exercise: Implement a guardrail that prevents the agent
# from executing dangerous shell commands

from agents import Agent, OutputGuardrail, GuardrailFunctionOutput, Runner
from pydantic import BaseModel

class SafetyCheck(BaseModel):
    is_safe: bool
    reason: str

# TODO: Implement this guardrail
# It should check if the agent's output contains any
# dangerous commands (rm -rf, DROP TABLE, shutdown, etc.)
# Return tripwire_triggered=True if unsafe

@OutputGuardrail
async def command_safety_guardrail(ctx, agent, output):
    # YOUR CODE HERE
    # Hint: Create a safety-checking agent with output_type=SafetyCheck
    pass

# Test your guardrail with these cases:
# Safe: "I'll list the files with ls -la"
# Unsafe: "Let me clean up with rm -rf /"

Key principle: Every exercise should have a clear success criterion. Students should know immediately whether their implementation works.

Assessment Strategies

Traditional quizzes are poor assessors of agentic AI skills. Instead, use practical assessments.

Code review assessments. Give students a broken agent system and ask them to identify and fix the issues. This tests debugging skills and pattern recognition simultaneously.

Architecture challenges. Present a business requirement and ask students to design an agent system — choosing how many agents, what tools, what guardrails, and what evaluation strategy. Grade on reasoning, not on matching a single correct answer.

Capstone project. Students build and deploy their own multi-agent system solving a real problem of their choice. They present it to the class, explaining design decisions and trade-offs.

Workshop Format That Works

For a three-hour workshop:

Time	Activity
0:00-0:20	Concept introduction with live demo
0:20-0:50	Guided exercise (scaffold provided)
0:50-1:00	Break
1:00-1:40	Independent exercise (less scaffolding)
1:40-2:10	Share solutions and discuss trade-offs
2:10-2:30	Advanced extension for fast learners
2:30-3:00	Wrap-up, Q&A, and next steps

Building Community

The most successful AI educators build communities, not just audiences. Create a Discord or Slack where students can ask questions, share projects, and help each other.

Post your workshop materials publicly on GitHub. This serves as marketing for your next workshop and builds trust with potential students. Include exercise solutions in a separate branch so students cannot accidentally see them during the workshop.

Monetization Paths

Once you have taught a few free workshops and built a reputation, consider these revenue models: paid cohort-based courses (highest engagement), self-paced video courses (highest scale), corporate training (highest per-session revenue), and one-on-one mentoring (highest per-hour rate).

FAQ

What is the right group size for an agentic AI workshop?

For hands-on workshops, keep groups between eight and twenty participants. Below eight, the peer learning dynamic weakens. Above twenty, you cannot give individual help when students get stuck on environment setup or debugging. If demand exceeds twenty, add a teaching assistant for every ten additional students.

How do I handle students with very different skill levels?

Design exercises with a base requirement and optional extensions. The base requirement should be achievable by beginners, while extensions challenge advanced students. For example, the base exercise might be "build an agent with one tool," while the extension is "add a guardrail and structured output."

Should I teach a specific framework or keep it framework-agnostic?

Teach a specific framework. Framework-agnostic courses tend to be too abstract for students to apply immediately. Pick one framework, teach it well, and note where concepts transfer to other frameworks. Students can generalize once they have mastered one concrete implementation.

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