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
Week 1 · Foundations

Introduction to Agentic AI — and what enterprises actually use.

Everyone uses AI; few profit from it yet. Agents — systems where the model owns the control flow — are the bet the industry is making to close that gap. This week builds your mental model: the loop, the vocabulary, the autonomy dial, and the 2026 landscape.

In the news · 2026

Five numbers that frame the semester

Click each card for the “so what” and the source. All figures are approximate and dated — that’s a feature of this field, not a bug of these slides.

TAP
88%
of firms use AI regularly — but only ~39% report EBIT impact
Everyone uses AI; few profit yet. Agents are the industry’s bridge from usage to value — and the paradox this course exists to close.
McKinsey, State of AI in 2025 (2025), approx.
TAP
40%
of enterprise apps expected to ship task-specific agents by end of 2026 — up from <5% in 2025
You are early. The skill you build this semester is arriving in the software you’ll use at work.
Gartner (2025), projection, approx.
TAP
4+
live agentic-commerce efforts: Visa + OpenAI, ChatGPT Instant Checkout, Amazon “Buy for Me,” Mastercard Agent Pay
Software can now hold a wallet. What goes right — and what goes wrong — when an agent can transact?
Digital Commerce 360 (2026)
TAP
80–90%
of a real cyber-espionage campaign was automated after a state-linked group jailbroke an agentic coding tool (~30 targets)
Autonomy cuts both ways. Power without guardrails is a liability — this previews our security week.
Anthropic / Cybersecurity Dive (Nov 2025)
TAP
−14%
hiring rate for 22–25-year-olds in AI-exposed jobs — yet ~57% of AI use augments rather than automates
It’s not replacing you; it rewards the people who can use it well. That’s the career case for this course.
Anthropic, Labor-Market Impacts / Economic Index (2026), approx.
The tension

The 2026 split: exploding capability, stubborn unreliability

Advancing fast

Task length doubles ~every 88 days

The length of tasks agents can complete autonomously has been doubling roughly every three months (METR, approx.). Demos get dramatically better every semester.

Still unreliable

~20% vs 72%

On long computer-use tasks, top agents score around 20% where humans score ~72% (OSWorld, approx.). Benchmarks lag the demos — reliability is the semester’s recurring villain.

Both of these are true at once. Holding that tension — capability and unreliability — is the whole discipline of agentic AI.
This week

Work through the four parts

01 FoundationsFrom LLM to agentWhat a language model actually is, why one call can’t book a flight, the four parts of an agent — and a step-through agent-loop simulator. → 02 VocabularyAgent vs workflow vs chatbotThe real distinction — who owns control flow — plus the five workflow patterns and an autonomy dial you can actually turn. → 03 LandscapeWhat enterprises actually use in 2026The agent stack, build vs buy vs wait, MCP & A2A, why coding agents broke out first — and the gen-AI paradox case. → 04 PracticeExercises, toolchain, interview checkClassify seven scenarios, trace a loop by hand, set up Gemini + Copilot for free, and test yourself against interview-level questions. →
Objectives

By the end of Week 1 you can…

← BackCourse home