04 · Practice & Case — CIS 4394 Week 4

Exercise A

Annotate the transcript

👥 Group task · Pairs · ~10 minutes

Below is the full trace of a travel-cost agent answering: “Can I do 3 nights in Boston — flight from Atlanta plus hotel — for under $900?” Mark up a copy (or a screenshot): (1) circle every point where the loop made a decision; (2) underline the observation that changed the plan; (3) find one wasted step; (4) verdict: is this plan-then-execute or interleaved — and how can you tell from the trace alone?

Produce: your annotated trace + a one-sentence verdict with evidence.

Thought 1: Budget question. Plan: find flight price, find hotel price ×3, add, compare to 900. Action 1: Search("ATL to BOS round trip flight price this weekend") Obs 1: Round trips from $228. Thought 2: Now hotels near downtown Boston. Action 2: Search("Boston downtown hotel price per night") Obs 2: Downtown averages ~$310/night. Cambridge side ~$185/night. Thought 3: 310 × 3 = 930 already busts the budget before airfare. But the observation offered a cheaper substitute — switch to Cambridge. Action 3: Search("cheapest month to visit Boston") Obs 3: January–February are cheapest. Thought 4: That doesn't help — the user asked about THIS weekend. Back to the math. Action 4: Calculator("228 + 3*185") Obs 4: 783 Thought 5: 783 < 900, with $117 slack. Goal satisfied → stop. Answer: Yes — about $783: $228 flights + 3 nights in Cambridge at ~$185. Downtown would bust the budget.

(1) Decisions: Thought 1 (decompose + set the plan), Thought 3 (accept the Cambridge substitute — the big one), Thought 4 (recognize a dead end and return to plan), Thought 5 (stop check: goal satisfied). (2) The plan-changing observation: Obs 2 — the agent came for one number and the environment offered a better option; feeding that back is what a one-shot answer can never do. (3) The wasted step: Action 3. “Cheapest month” doesn't serve a this-weekend goal — a step cap or a sharper Thought 3 would have saved one call. Honest traces show waste; that's a feature. (4) Verdict: interleaved (ReAct-style), with a light plan on top. The tell: Action 4 depends on the content of Obs 2 (185 wasn't knowable when the plan was made). In strict plan-then-execute, Thought 1's plan would have been executed verbatim — and would have concluded “no, $930+ busts it.”

Exercise B

Draw the state graph

👥 Group task · Pairs · ~15 minutes

On paper or a whiteboard, design a LangGraph-style graph for an agent that (1) makes a plan, (2) executes one step, (3) reflects on the result, and loops or finishes. Your drawing must label: the state fields that persist across nodes (e.g., task, plan, past_steps, results, critique); every node; which edges are fixed vs conditional; and the stopping condition that prevents an infinite loop.

Produce: a photo of the graph + one sentence: where would ReAct collapse two of your nodes into one?

Fixed edges: START→planner, planner→executor, executor→reflector. Conditional edge: reflector — routes back to executor (critique says “not done”, steps < N) or to END (done, or the max-iteration guard fires at N). The ReAct collapse: ReAct merges planner + executor into one looping “model” node that re-decides the next step every turn — you trade the reviewable up-front plan for adaptivity. Common miss: teams forget steps in the state — a stopping condition that isn't stored anywhere can't be checked.

Case study · fictional teaching case

Planning beats greedy generation

A travel startup ships an itinerary agent: “3 cities in Italy, 7 days, total budget $2,500.” Two builds, two outcomes.

Build 1 — greedy one-shot

One big prompt, one answer. The model books Rome ($1,100 with a boutique hotel it “liked”), then Florence ($900)… and reaches Venice with $500 left against ~$800 of real costs. The itinerary reads beautifully — and busts the budget by ~$300, discovered by the customer.

Build 2 — plan, execute, check

First a plan node allocates the budget (roughly $833/city) — reviewable before anything is “booked.” An executor prices each city with tool calls; a checker node compares running total vs plan after every step. Rome comes back at $950 → the checker routes back to replan: downgrade the hotel, shift $100 from Florence. Final: $2,470, under budget, with a trace showing every correction.

Discussion question 1Which reasoning pattern is Build 2 — and what, exactly, did the up-front plan buy that Build 1's fluent answer couldn't?

Plan-then-execute plus a reflection/check loop (the checker is a lightweight reflector). The plan bought a global constraint: $2,500 allocated across cities before any city could greedily overspend. Greedy generation optimizes each step locally; nothing in Build 1 ever “sees” the whole budget at once. The check node bought error detection mid-run — the $950 Rome quote was caught in step 2, not in the customer's inbox.

Discussion question 2The startup adds real booking (money moves). Where does the human gate go in Build 2's graph — and what should the approval screen show?

On the edge into any node that spends — after the checker approves the plan-conforming price, before the booking tool executes. One gate for the full itinerary (batch approval) if bookings are refundable; per-booking gates if not. The screen should show the decision, not the vibes: item, price, remaining budget before/after, which plan line it satisfies, and what the agent will do next if declined. That's the Approve/Edit/Reject pattern from your GA1 rubric, scaled to money.

Discussion question 3Compliance asks: “prove the agent never exceeded budget.” What must Build 2 have logged, per step, for that sentence to be provable?

Per step: state before (remaining budget), the observation (the price quote), the available actions, the selected action + arguments, the result, and state after — plus the checker's verdict at each pass and the final stop reason. That's the Week 2 evidence standard again, now as a compliance artifact: with it, “never exceeded budget” is a query over the trace; without it, it's a promise.

Next week

Quiz 2 — reasoning, planning & the loop

Five multiple-choice questions, closed-book, in class. Everything comes from the readings listed above — scope: the Week 4 readings (Kaggle whitepaper · Intro to LangGraph · ReAct skim) + the Week 5 reading (Ponnambalam, LangGraph, LinkedIn Learning) — plus this week's core ideas. Try the samples before revealing; the real quiz is the same style and difficulty.

Five sample questions

Same style and difficulty as the real thing.

Read a loop. Draw a loop. Budget a loop.

Annotate the transcript

Draw the state graph

Planning beats greedy generation

Interview check

This week's readings

Quiz 2 — reasoning, planning & the loop

Five sample questions