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 4 ⌂ Course 01The Agent Loop 02Reasoning Patterns 03LangGraph 04Practice & Case
01 · Foundations

The agent loop.

A chatbot answers the last message. An agent pursues a goal across steps — observing, reasoning, acting, and checking whether it's done. That cycle is the unit of agent behavior, and everything else this semester elaborates it.

Two postures

Reactive vs goal-oriented

Reactive — a chatbot
You: How tall is the Statue of Liberty? Bot: About 93 meters to the torch. You: And Atlanta's tallest building? Bot: Bank of America Plaza, about 312 m. (…you do the comparison yourself)

Each turn stands alone. The user owns the goal, the plan, and the follow-through.

Goal-oriented — an agent
Goal: "Which is taller: Atlanta's tallest building, or 2× the Statue of Liberty?" The agent decomposes, calls tools, keeps STATE across steps, and STOPS when the goal is satisfied — not when you stop typing.

Three defining pieces: a goal, persistent state, and a termination condition (Wang et al., 2023 survey framing).

Interactive · step through a real episode

One goal, one loop, eight steps

This is the loop diagram from Week 2 — same shape, new episode. Press Step and watch the agent answer the Atlanta-vs-statue question with two tools: Search and Calculator.

Observe Reason Act (call a tool) Update state Done? → Finish

The amber node is the stop check: after every state update, the loop asks whether the goal is satisfied — or whether it has run out of budget.

Episode: the height question · step 0 of 10
Press Step to begin the episode…
Agent state
Read the trace like an auditor: every step has the shape state before → observation → selected action → result → state after. That's the evidence standard from Week 2 — and it exists precisely because the loop makes each decision visible.
Zoom in

Anatomy of one loop turn

  1. Assemble the input. The harness packs the goal + current state + latest observations into a prompt (Week 3's context engineering, applied every turn).
  2. The LLM decides. Given what it sees, it picks exactly one thing: call a specific tool with specific arguments — or declare the goal met and finish.
  3. The harness executes. Tool runs, result comes back. The model never touches the environment directly; the harness does (and can refuse — Week 2's guardrails).
  4. State updates. The observation is appended; facts learned are recorded. This is what makes step 5 smarter than step 2.
  5. The stop check. Goal satisfied? Step cap hit? Budget burned? If yes, exit; if no, go to 1.
Notice who owns what: the model owns step 2. The harness — code you write — owns 1, 3, 4, and 5. Four fifths of the loop is ordinary software you can test.
Discussion
Discussion questionYour agent can call send_payment(). Looking at the five parts of a loop turn — where exactly would you put the human gate, and why not anywhere else?
Between step 2 and step 3 — after the model has committed to a specific action with specific arguments, before the harness executes it. Earlier (before reasoning) you'd interrupt harmless steps too; later (after execution) the money is already gone. The gate should also be selective: pause only for irreversible or spend actions, or approval fatigue will train humans to click yes. This is exactly where Codex's exec_policy sits in the Week 2 material.
Concept check

Loop or no loop?

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