Tasks / Operate

Make the launch call

Can the model make a clear go / no-go call from mixed launch evidence, checked against the criteria agreed up front?

Measures the modelTask v1.2 · 2 casesDifficulty

What AI gets right here, and what you’ll still have to catch

From 14 graded outputs by 7 models. 64% were usable with at most a quick edit.

Reliably right

  1. Finds the Scotland breach100% pass
    Clearly identifies Scotland's refund breach (66% above control, 143% after supplier change) and acceptance dip below 80%, correctly holds Scotland.
    Opus 5.5 · Claude · Go/no-go for AI substitutions, from the rollout data
  2. Produces the required deliverable98% pass
    The recommendation is in the requested form, for the meeting, within the length, and includes necessary steps a PM could act on.
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies
  3. Addresses the actual decision95% pass
    The output gives a clear, unambiguous 'no-go' call early, and states what would change it (fixes and retest).
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies

Where it slips

  1. Limits the downside of being wrong32% pass
    No specific post-launch monitoring signal or threshold is named; only generic 'monitoring' and a kill switch are mentioned.
    GPT-6 Astra · ChatGPT · Go/no-go for AI-drafted support replies
  2. Catches the duplicate rows50% pass
    Finds and removes the duplicate rows but never states whether they change the results.
    GPT-6.1 Sol · API · Go/no-go for AI substitutions, from the rollout data
  3. Uses the supplied evidence correctly54% pass
    The output states that pilot agents were hands-on, engaged and motivated, which is not supported by the supplied context (only that 7/8 wanted to keep it).
    Opus 5.5 · Claude · Go/no-go for AI-drafted support replies

Case viewer

Read the brief, then put up to three outputs side by side, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

Tomorrow's launch meeting decides whether AI-drafted support replies go live to all 42 support agents on Monday. You're the PM. Using the material below, write your launch recommendation for the meeting: go, no-go, or go with conditions, and why. Keep it under 400 words.

What the model was given6 items: Scenario, Launch criteria agreed in the PRD, Pilot (8 agents, 3 weeks, 2,940 tickets), Eval set (500 tickets), Open bugs, Support operations
ScenarioLedgerly is accounting software for small businesses. The feature drafts a first reply to each support ticket; an agent reviews and edits every draft before it is sent.
Launch criteria agreed in the PRD1. At least 60% of drafts accepted as-is or with minor edits. 2. Wrong or harmful drafts (wrong account data, wrong policy, invented features) at most 1% on the 500-ticket eval set. 3. No draft may commit to a refund. 4. Median time to first response down at least 30% in the pilot.
Pilot (8 agents, 3 weeks, 2,940 tickets)Accepted as-is or with minor edits: 64%. Median time to first response: 7.1 hours before, 4.2 hours during (−41%). Seven of the eight agents want to keep it.
Eval set (500 tickets)Wrong or harmful drafts: 9 of 500 (1.8%). Six of the nine are billing tickets quoting the old prices, which changed on 1 September; the model's reference data stops in August. Two drafts said “we'll refund this charge”; the pilot agents caught and removed both.
Open bugsReplies to Welsh-language tickets come back in English (about 0.3% of tickets). Tables in drafts are badly formatted.
Support operationsTraining for all 42 agents is booked for Friday. Billing is one of four queues and handles 38% of tickets.
What a strong answer doesThe answer key the graders mark against

Not a full go: criterion 2 fails (1.8% against 1%) and criterion 3 is breached even though agents caught it. Strong answer: go with conditions, launching to the non-billing queues while billing waits for updated pricing data and a hard block on refund language, then re-running the eval to confirm it meets the bar; names exactly what would turn it into a full go.

Critical failures (cap the score at 40)
  • Recommends a full launch while the error rate misses the agreed 1% bar
  • Treats the refund commitments as acceptable
Case

Staff-level · v1.9 · synthetic · AI product, launch

The outputs

7 setups answered this brief. Pick up to three to read side by side.

Needs more than a quick edit3 of 10 checks passed

A PM would need to re-check the non-billing error rate, refund-draft queue attribution, and monitoring/rollback plan before using it. The core recommendation is useful but not evidence-safe as written.

Output

Recommendation: Go with Conditions

While the pilot demonstrated strong operational impact—reducing median first-response time by 41% (beating our 30% target) with a 64% acceptance rate—we failed two mandatory launch criteria:

  1. Accuracy (Criterion 2): Harmful/wrong drafts hit 1.8% (9/500) versus the $\le$1.0% threshold.
  2. Refund commitments (Criterion 3): Two drafts explicitly promised refunds, violating our zero-tolerance policy.

However, these failures are isolated and addressable. Six of the nine errors were caused by August reference data lacking the September 1 price changes, and both refund commitments originated in billing workflows.

Rather than cancelling Friday’s 42-agent training or delaying the productivity benefits across the entire organization, we should launch on Monday under the following three conditions:

Launch Conditions

1. Disable AI drafting on the Billing queue for Monday: Billing represents 38% of volume and contains almost all our critical failure points (pricing errors and refund risks). We will launch exclusively to the remaining three queues (62% of ticket volume), where accuracy meets our threshold. 2. Implement deterministic refund guardrails by Friday: Deploy a hard system-prompt restriction and a post-generation regex/keyword filter that blocks or drops drafts containing refund commitments before reaching the agent. 3. Update agent training on Friday: Keep Friday’s training on schedule. Explicitly train agents on: - The pilot's phased rollout (Billing queue excluded initially). - Known minor bugs (Welsh-language tickets defaulting to English; manual table reformatting required). - Strict agent accountability as the final human reviewer for every sent message.

Path to Full Billing Rollout

Engineering will ingest the post-September 1 pricing documentation and run a targeted 200-ticket eval on billing drafts next week. Once billing tickets achieve $<1\%$ error and 0 refund promises, we will enable the feature for the billing queue.

This approach protects Ledgerly’s financial liability and customer trust while immediately capturing significant speed improvements for over 60% of our support volume.

Claims the judge couldn’t find in the brief

  • Both refund commitments originated in billing workflows.
  • Billing contains almost all critical failure points, including pricing errors and refund risks.
  • The remaining three queues meet the accuracy threshold.

Grades and run details

Decision model 90 · LLM judge 3 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly45%
  • passAddresses the actual decision100%
  • passRespects explicit constraints54%
  • passIdentifies material uncertainty96%
  • partialAvoids unsupported claims27%
  • passProduces the required deliverable96%
  • passChecks every agreed criterion24%
  • passMakes a clear call100%
  • partialLimits the downside of being wrong38%
  • passGets the base of every number right86%
Run
Run
#1
API response time
13 s
Submitted
1 Oct 2026

Usable with a quick edit9 of 10 checks passed

The reasoning is sound, evidence is correctly used, and the recommendation is clear. A PM could add a monitoring threshold in a quick edit without redoing any analysis.

Output

Recommendation: no-go for Monday’s rollout to all 42 agents. Reassess once the failed safety gates have been fixed and retested.

The pilot demonstrates value: 64% of drafts were accepted as-is or with minor edits, exceeding the 60% target. Median first-response time fell 41%, exceeding the 30% target, although the before-and-after comparison does not establish causality. Seven of eight agents want to keep it.

But we failed two explicit launch criteria:

  • Wrong or harmful drafts: 1.8% against a 1% ceiling. Nine failures is above the maximum five permitted on the 500-ticket set.
  • No refund commitments: failed. Two drafts promised refunds. Agents catching them prevented those messages reaching customers; it does not satisfy a criterion about what drafts may say. Human review was already part of the agreed design.

Six errors used outdated billing prices. That gives us a concrete fix, but billing represents 38% of tickets, so this is a substantial exposure. Updating the reference data alone would not establish that the remaining errors or refund promises are resolved.

Required before reconsidering launch:

  1. Engineering and the billing policy owner: update and verify current prices and policies, with a named owner and process for keeping reference data current. Investigate the other three harmful drafts.
  2. Engineering: add and test a control that prevents refund commitments from appearing in agent-facing drafts, including paraphrases.
  3. PM and evaluation owner: rerun the full 500-ticket evaluation after the fixes, meeting the original thresholds, and add targeted pricing and refund tests. Report those additional results separately.

Excluding billing is a possible narrower release, not an automatic pass: we lack queue-level results and do not know whether refund failures are confined to billing. It would require explicit scope approval and validation of both routing and the remaining queues.

Welsh-language tickets should bypass drafting until language handling is fixed. Disable tables or use plain text pending the formatting fix.

Keep Friday’s training as preparation, with launch clearly marked as pending. The booking should not determine readiness. After the gates pass, expand in stages with monitoring and a named owner able to disable drafting.

Grades and run details

Decision model 95 · LLM judge 10 of 11 checks
Decision model checks
  • passUses the supplied evidence correctly69%
  • passAddresses the actual decision94%
  • passRespects explicit constraints44%
  • passIdentifies material uncertainty97%
  • passAvoids unsupported claims74%
  • passProduces the required deliverable86%
  • passChecks every agreed criterion49%
  • passMakes a clear call93%
  • partialLimits the downside of being wrong54%
  • passGets the base of every number right83%
Run
Run
#1
Time to output
34 s
Submitted
25 Sept 2026

Usable with a quick edit7 of 10 checks passed

The unsupported claim about pilot engagement is minor; remove it and the recommendation remains sound and usable with a quick edit.

Output

Launch recommendation: AI-drafted support replies

Recommendation: no-go for all 42 agents this Monday. Go the following Monday (5 Oct) if three conditions are met.

Scorecard against PRD criteria

CriterionTargetResultStatus
Accepted as-is / minor edits≥60%64%✅ Pass
Wrong or harmful drafts≤1%1.8% (9/500)❌ Fail
No refund commitments02 drafts❌ Fail
Median first response−30%−41% (7.1h → 4.2h)✅ Pass

Two of four criteria fail. The value is proven, but the safety bar isn't met yet.

Why not Monday

  • Refunds are a hard line, and we crossed it. Two drafts promised refunds. Agents caught both, but the criterion says no draft may do this. Human review is the backstop, not the control. The pilot used 8 motivated agents. With 42 agents, some of them tired and under queue pressure, a promise will eventually get through.2
  • Billing errors land on our biggest queue. Six of the nine failures quote pre-1 September prices. Billing is 38% of volume, so this is a systematic error, not an edge case.
  • Training comes after launch. Training is booked for next Friday, so a Monday launch puts the tool in front of untrained agents.1 The pilot agents were hands-on and engaged, and we can't assume the rest will review as carefully.

Conditions for a 5 October go

  1. Refund guardrail. Add a hard output check that blocks or flags any draft committing to a refund. Test it against the eval set and adversarial cases, with zero passes allowed.
  2. Refresh pricing data and re-run the full 500-ticket eval. Launch only if wrong or harmful drafts are at most 1%. Removing the six pricing errors would leave 0.6% on paper. We should prove that with a re-run, not assume it.
  3. Train before rollout. Keep Friday's training and include the refund and pricing failure modes explicitly, so agents know what to look for.

Not blocking

  • Welsh replies coming back in English (0.3%). Turn drafting off for Welsh-language tickets until this is fixed. It costs little and avoids a visible failure.
  • Table formatting. This is cosmetic, and agents edit every draft anyway. Fix it in the normal backlog.

Meanwhile

Keep the 8 pilot agents on the tool so we don't lose momentum.3 In the first two weeks after launch, spot-check a sample of sent billing replies.

The ask

Agree to the three conditions. Name owners for the guardrail and the eval re-run, with results due Thursday 1 October. If both pass, we confirm go on 2 October after training.

What a PM had to fix

  1. 1Invented evidenceVerify or remove the claimTargeted repair

    Source: Support operationsTraining for all 42 agents is booked for Friday.

    What we’d changeThe brief says training is on Friday, not 'next Friday'. Don't turn an ambiguous date into a blocker, or build an October schedule around it.

  2. 2Hypothesis stated as factReframe it as a hypothesisQuick edit

    What we’d changeSay the risk grows with 42 agents rather than stating it as certain, and don't assume the pilot agents were unusually motivated.

  3. 3Constraint missedRestore the constraintQuick edit

    What we’d changeMomentum isn't a reason to keep a tool that fails two criteria in use. Add the refund check first, or pause it.

Claims the judge couldn’t find in the brief

  • Pilot agents were hands-on, engaged and motivated

Grades and run details

Decision model 80 · LLM judge 8 of 11 checks
Decision model checks
  • failUses the supplied evidence correctly21%
  • passAddresses the actual decision96%
  • passRespects explicit constraints32%
  • passIdentifies material uncertainty98%
  • partialAvoids unsupported claims41%
  • passProduces the required deliverable65%
  • passChecks every agreed criterion99%
  • passMakes a clear call100%
  • partialLimits the downside of being wrong81%
  • passGets the base of every number right81%
Run
Run
#1
Time to output
23 s
Submitted
25 Sept 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 6

Uses the supplied evidence correctlyMixedRightWrong
Gemini 3.8 Flash · API

It invents or assumes that both refund drafts were billing-related, that non-billing queues meet the accuracy threshold, and that billing contains almost all critical failure points.

GPT-6 Astra · ChatGPT

All factual statements about the current situation are directly from the supplied brief or context, or follow from arithmetic; no invented facts.

Opus 5.5 · Claude

The output states that pilot agents were hands-on, engaged and motivated, which is not supported by the supplied context (only that 7/8 wanted to keep it).

Respects explicit constraintsMixedRightRight
Gemini 3.8 Flash · API

The proposed non-billing launch is not shown to enforce the 1% wrong/harmful criterion, and the refund guardrail proposal is not evidenced as sufficient.

GPT-6 Astra · ChatGPT

The output is a launch recommendation, addresses the meeting, and stays within the 400-word limit (345 words).

Opus 5.5 · Claude

Respects the length limit, addresses the meeting, and proposes conditions that would enforce the launch criteria.

Identifies material uncertaintyMixedRightRight
Gemini 3.8 Flash · API

It does not name the key unknowns that could change the call, such as the non-billing error rate, whether refund drafts were billing-related, or whether guardrails actually prevent refund commitments.

GPT-6 Astra · ChatGPT

It identifies the lack of queue-level results and the unknown scope of refund failures, and explains how those would be resolved.

Opus 5.5 · Claude

Names the remaining work (guardrail, pricing refresh, re-run) and says the call depends on their results.

Avoids unsupported claimsWrongRightWrong
Gemini 3.8 Flash · API

It presents unsupported causal and coverage claims as fact, including that refund commitments originated in billing workflows and that non-billing accuracy meets the threshold.

GPT-6 Astra · ChatGPT

The note about causality is framed as a methodological observation, not as a factual claim; no unsupported causes or forecasts are presented as fact.

Opus 5.5 · Claude

Presents pilot agents' hands-on engagement and motivation as established fact when the evidence only shows retention preference, not engagement level.

Checks every agreed criterionMixedRightRight
Gemini 3.8 Flash · API

It checks criteria 2, 3, and 4, but does not explicitly test criterion 1 against the 60% acceptance bar.

GPT-6 Astra · ChatGPT

All four launch criteria are explicitly assessed: acceptance rate, error rate, refund commitment, and time reduction.

Opus 5.5 · Claude

Evaluates all four PRD criteria with a status table and references to the evidence.

Gets the base of every number rightMixedRightRight
Gemini 3.8 Flash · API

It uses the 62% volume base correctly but treats non-billing accuracy as meeting the threshold without a verified denominator or error count.

GPT-6 Astra · ChatGPT

All derived figures (41% time reduction, 1.8% error rate, 9/500, 38% of tickets) are computed correctly and bases are clear.

Opus 5.5 · Claude

All percentages and differences are computed correctly from the supplied numbers.

All got wrong 1

Limits the downside of being wrongWrongWrongWrong
Gemini 3.8 Flash · API

It does not specify post-launch monitoring, a pause or rollback threshold, or irreversible customer-communication risks.

GPT-6 Astra · ChatGPT

No specific post-launch monitoring signal or threshold is named; only generic 'monitoring' and a kill switch are mentioned.

Opus 5.5 · Claude

Post-launch spot-checks are mentioned but no threshold or rollback action is specified, and irreversible risks are not flagged.

All got right 3

Addresses the actual decisionRightRightRight
Gemini 3.8 Flash · API

It clearly recommends go with conditions for Monday and states the billing re-evaluation result that would enable full rollout.

GPT-6 Astra · ChatGPT

The output gives a clear, unambiguous 'no-go' call early, and states what would change it (fixes and retest).

Opus 5.5 · Claude

Commits to no-go for Monday, with a conditional go the following Monday, and states what would change the call.

Produces the required deliverableRightRightRight
Gemini 3.8 Flash · API

It is a concise launch recommendation in the requested form and appears usable for the meeting with light edits.

GPT-6 Astra · ChatGPT

The recommendation is in the requested form, for the meeting, within the length, and includes necessary steps a PM could act on.

Opus 5.5 · Claude

Delivers a complete launch recommendation under 400 words that the meeting could act on.

Makes a clear callRightRightRight
Gemini 3.8 Flash · API

It makes a clear go-with-conditions call and states the billing rollout condition.

GPT-6 Astra · ChatGPT

Clear 'no-go' call up front, with a statement that reassessment follows fixes and retesting.

Opus 5.5 · Claude

Clear no-go for Monday, go on 5 October if three named conditions are met.

Results

Every setup we’ve tested on this task, across all cases and repeats, graded on the current checklist. Calibrated: the graders match our PM on 87% of checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6.1 SolwithAPI87.388.32None
2GPT-6 AstrawithChatGPT86.088.32None
3Sonnet 5.5withAPI85.488.32None
4Opus 5.5withClaude80.486.42None
5GPT-6 LunawithAPI82.177.62None
6Gemini 3.8 FlashwithAPI71.931.521 capped
7Gemini 3.5 Flash-LitewithGemini50.642.22None

About the task

The PM job

Deciding whether a feature ships on the planned date, and on what conditions.

Why it matters

Launch meetings reward optimism. A good call checks each agreed criterion, weighs a real risk against a real gain, and says exactly what would change the answer. A weak one rubber-stamps the launch or blocks it on noise.

What good looks like

  • Checks each agreed criterion against the evidence
  • Makes one clear call, with conditions if needed
  • Separates risks that block launch from risks that can be managed
  • Says what would change the call
  • Says how to limit the damage if the call is wrong

Deliberately not measured

  • Rollout engineering detail
  • Project-plan formatting
Capability tested

Deciding against agreed launch criteria

The failure we’re looking for

Rubber-stamps a launch that misses an agreed bar, or blocks it on noise

Grading

Decision model and LLM judge, calibrated against a blind PM review

Variants

A launch memo from supplied evidence · Staff level: a regional call from an attached data workbook