Georgia State University — J. Mack Robinson College of Business PATH — Pathways for AI Training & Hiring CIS 4394 Agentic AI  ·  Fall 2026  ·  Dr. Xinyu Fu
CIS 4394 · Week 1 ⌂ Course 01LLM → Agent 02Agent vs Workflow 03Enterprise 2026 04Exercises & Setup
04 · Practice

Now you make the calls.

Classify real scenarios, trace a loop by hand, stand up the free toolchain, and check yourself against interview-level questions.

Exercise A

Classify the scenario

For each business scenario, pick the lowest level that genuinely solves it: rules (no LLM), a single LLM call, a workflow, or an agent. Answers are “suggested” — in the debrief, your reasoning counts more than the label.

Exercise B

Trace the loop by hand

Task: “Book a room for 6 people next Tuesday afternoon.” On paper (or in your notes), write the observe → reason → act → observe trace. Mark the stop condition and where a human gate belongs. Then compare.

OBSERVE goal: room for 6, Tue afternoon. Rooms + calendars unknown. REASON need candidate rooms before times → query room system ACT rooms.search(capacity>=6) → [Maple, Peachtree, 201] OBSERVE three candidates returned REASON check Tuesday-afternoon availability for each ACT rooms.availability([...], tue_pm) → Maple 2–4, 201 3–5 REASON two options; Maple earlier + has a screen → propose Maple 2pm HUMAN GATE ⏸ "Book Maple, Tue 2–2:30?" → approve ACT rooms.book(maple, tue_1400) → confirmed STOP ✓ a confirmed booking exists ← the stop condition

What graders look for: the loop actually loops (an observation changes the next action); the stop condition is named, not implied; the external action (booking) sits behind the human gate. A trace with no stop condition is a red flag.

Hands-on

Your toolchain — three pieces, all free

Step 1

Gemini free API key

Google AI Studio → create API key. No billing required; free-tier rate limits are fine for class. Keep it secret — never commit it to a repo. Already have an old Gemini key? Create a fresh one for this course.

aistudio.google.com/apikey ↗
Step 2

GitHub Copilot

The free plan includes agent mode; verified students get Copilot Pro via the Student Developer Pack. Enable it in VS Code.

Student Developer Pack ↗
Step 3

Hello, agent

A minimal loop — model, one tool, stop condition:

while not done: thought = llm(context) action = parse(thought) result = run_tool(action) context += result done = stop_condition(result)
Expect it to work ~most of the time. That gap between “demo” and “dependable” is not your bug — it is the field’s central open problem, and it is where this course spends Weeks 8–10.
Before next week

Assignment & logistics

Do

Coming up

Week 2 — Learning from the Best: Unpacking Coding Agents. We reverse-engineer Codex, apply the lens to Claude Code, and build a Job Search Agent three ways. Agent Radar begins (student news briefings). GA1 releases.

Go to the Week 2 site →
🎯 Take it to your final project: your capstone must be a real agent — the LLM owns control flow — with a named stop condition and one human gate. Write the one-line justification now: “This must be an agent, not a workflow, because …”
Interview check

Could you answer these in a job interview?

Each question is fair game in a 2026 tech or consulting interview. Try answering out loud, then reveal what a strong answer covers.

Take-aways

Week 1 in five lines

  1. An LLM predicts tokens; an agent wraps it with tools, a loop, and memory so it can act.
  2. The distinction that matters: who owns control flow — a developer (workflow) or the model at runtime (agent).
  3. Autonomy is a dial; choose the lowest level that solves the problem.
  4. Enterprises adopted AI everywhere but profit thinly — agents are the bet to close that gap, and reliability is the obstacle.
  5. Every real agent needs a stop condition, a human gate, and an honest evaluation.
Next week: Learning from the Best — Unpacking Coding Agents. We reverse-engineer Codex, apply the same lens to Claude Code, and start building the Job Search Agent (GA1 releases).

Week 2 readings:
· Codex’s real system prompt — gpt_5_2_prompt.md ↗
· OpenAI Cookbook — Planning for Long-Horizon Agents ↗
· Anthropic — Claude Code auto mode ↗
Read them with Week 1’s question in mind — who owns control flow? — and start on the Week 2 prep page.
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