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 · Agentic AI Weeks News Materials PATH About
GSU · Fall 2026 · CIS 4394

Agentic AI: build systems that reason, act, and know when to stop.

Generative AI is evolving into agentic AI — systems that reason, plan, adapt, and collaborate in complex environments. This course teaches the foundations and the practice: from the agent loop and tool use to evaluation, safety, and deployment, ending in a real capstone agent.

InstructorDr. Xinyu Fu
SchoolRobinson College of Business, GSU
FormatWeekly hands-on studio classes
ToolsAll free — no paid API keys
News & opportunities

Announcements

📣 Student events · AIS GSU Student Chapter · posted Sept 8, 2026

Breaking into consulting — two AIS panels: KPMG & Capgemini

The Association for Information Systems (AIS) GSU Student Chapter is hosting two interactive panels with consultants from major firms — advice on the skills that stand out in today's market, a day-in-the-life view of consulting careers, tips on marketing yourself, and direct networking.

🗓 KPMG Consulting panel (Leadership Engagement Series) — Monday Sept 21, 7:15 pm, 55 Park Place, Room 1242. Dinner provided for those who RSVP on PIN.
🗓 Breaking Into Consulting: Capgemini panel — Thursday Oct 1. Details and RSVP on PIN.

AIS × KPMG Leadership Engagement Series panel flyer — Monday September 21, 2026, 7:15 PM, 55 Park Place Room 1242; dinner provided for those who RSVP on PIN (QR code on flyer)

Scan the QR code on the flyer to RSVP · click to enlarge

AIS chapter on PIN — RSVP & event details ↗  ·  Browse PIN events ↗

📣 Hackathon · posted Sept 7, 2026 · submissions close Sept 14

AWS “Agents for Humans” Hackathon — $40,000 in cash prizes

A virtual hackathon from AWS: build an AI agent that solves a real problem for real people, using the Strands Agents SDK. Three tracks — 🏠 Everyday Agents (bills, appointments, life admin), 💼 Professional Agents (scheduling, follow-ups, reporting), 🤝 Good Neighbor Agents (help a nonprofit, school, or local org). Open to all skill levels worldwide, students included (country eligibility rules apply), and every participant who submits gets $50 in AWS credits. Winners announced Oct 14, 2026.

This is exactly the skill set you are practicing in this course — the Job Search Agent you're building for Group Assignment 1 is the same design problem: an agent that automates work nobody wants to do, with guardrails and a human in the loop. If your GA1 agent turns out well, polishing it into a hackathon entry is a very short step.

Read the announcement ↗  ·  Register & submit on DevPost ↗

Interactive class sites

The weeks

Each week ships as an interactive mini-site: clickable architecture explorers, step-through agent-loop simulators, live concept checks, hidden answers on every question, and full lab instructions. Readings and quiz scope are listed at the end of each week.

Module 1 · Foundations of agentic AI
Week 1 Introduction to Agentic AI + What Enterprises Actually Use What an LLM is (just enough) · the agent loop · agent vs workflow vs chatbot · the autonomy dial · the 2026 enterprise agent stack · classify-the-scenario exercises · toolchain setup. → Week 2 Learning from the Best: Unpacking Coding Agents Reverse-engineer Codex · the same lens on Claude Code · then build a Job Search Agent three ways (Google Opal, LangSmith Fleet, or Codex + Python) with tests, guardrails, and human approval. → Week 3 Prompting & Context Engineering Anatomy of a prompt (annotated & clickable) · few-shot, chain-of-thought, JSON output · a temperature dial · the context-window packing game · context rot · the $1 Chevy Tahoe case · Quiz 1 week. → Week 4 Reasoning, Planning & the Agent Loop The Observe→Reason→Act loop, step-through simulator · ReAct, plan-then-execute, reflection & Tree of Thoughts (with the papers) · the cost of thinking · build a minimal LangGraph agent with a max-iteration guard. →
Module 2 · Supercharging agents with tools & protocols
Week 5 Tool Use & Function Calling The “Act” arrow becomes a real tool call: the function-calling round trip · schemas as interfaces · errors as observations · the auto-run / constrain / gate decision · tools in LangGraph. → Week 6 MCP & A2A — the interoperability standards The N×M integration problem · MCP (agent ↔ tools) architecture, clickable · A2A (agent ↔ agent) and Agent Cards · which standard when · the trust boundary a third-party server sits inside. → Week 7 Memory, RAG & Knowledge Working, episodic, semantic & procedural memory · the RAG pipeline step by step · embeddings as geometry · grounding and citations · RAG vs fine-tuning, decided. →
Module 3 · Evaluation, safety & governance
Week 8 Evaluation & Reliability Why demos lie · outcome vs trajectory · pass@k vs pass^k (with a calculator you can push) · benchmark literacy · LLM-as-judge and its biases · writing a real eval set. → Week 9 Multi-Agent Systems Supervisor, network & hierarchical topologies, clickable · what actually gets shared · the honest case against multi-agent · why these systems fail · split-or-not decisions. → Week 10 Security, Trust & Governance The lethal trifecta as a toggle you can flip · indirect prompt injection as an architecture problem · least-privilege tools · NIST AI RMF vs the EU AI Act · attack-and-patch lab. →
Module 4 · Deployment & applications
Week 11 Agentic Commerce When the action moves money: irreversibility, identity & liability · the emerging payment protocols (AP2, ACP) · computer-use agents · the deployment checklist and the gate that must hold. → Weeks 12–14 Capstone studios, final presentations & course wrap-up Build time in class, then demo day. Published here as the semester reaches them.
How the course runs

Four modules, one capstone

Module 1

Foundations

LLMs → agents, the loop, agentic coding, prompting & context engineering, reasoning & planning.

Module 2

Tools & protocols

Tool use and function calling, MCP & A2A, memory & RAG.

Module 3

Eval, safety, governance

Evaluation & reliability, multi-agent systems, security & governance.

Module 4

Deployment & capstone

Deployment & agentic commerce, capstone studios, final presentations.

Build paths (all free): LangGraph on the Google Gemini free tier (code, default) · LangSmith Fleet (no-code) · GitHub Copilot as the coding assistant · LangSmith for evaluation and observability. The capstone must be a real agent — the LLM owns control flow — with a named stop condition, one human gate, an evaluation harness, and a security review.
Downloads

Class materials

Week 1 — lecture deck

Introduction to Agentic AI + What Enterprises Actually Use (PowerPoint, ~66 slides, with speaker notes and references).

Download .pptx ↓

Week 2 — class bundle

Lesson deck, step-by-step lab guide, starter kit (résumé, preferences, six-job dataset with a prompt-injection test, Python baseline), and pre-class prep.

Download .zip ↓
About

Who teaches this

Dr. Xinyu Fu

Instructor, Computer Information Systems, J. Mack Robinson College of Business, Georgia State University. This course pairs research-grounded foundations (ReAct, Toolformer, LLM-agent surveys, τ-bench) with what enterprises actually deploy — and every claim on these pages is dated and sourced, because agentic AI moves fast. The course is offered within PATH — Pathways for AI Training & Hiring, a national initiative led by MIT RAISE and Georgia State.

Faculty profile ↗  ·  Personal site ↗  ·  xfu11 [at] gsu [dot] edu

Why the sites are interactive

Agent loops, autonomy, and control flow are abstract until you can push on them. Each week's site turns the core abstractions into things students can click, step through, and break — the same standard the course applies to agents themselves: don't trust the demo, test the behavior.