▸case-04 Our frontend engineering team completed 34 out of 50 planned story points during Sprint 14. Burn-down chart shows tasks stalling midway through testing. Please conduct a standard Agile retrospective to calculate team velocity, analyze burndown drift, and suggest sprint planning adjustments for Sprint 15. | pass→fail | 15,573 | 22,419 | +44% | 1 | 1 | 0% | 2,451 | 4,215 | +72% | 0 | 0 | — |
▸case-10 Our data science platform is exhibiting multiple signs of operational degradation: job preemption errors, unindexed database queries, long artifact transfer times, and manual environment setups. Compile an initial operational symptom assessment listing these observed problems with supporting evidence. | pass→pass | 16,160 | 7,769 | -52% | 1 | 1 | 0% | 2,404 | 1,351 | -44% | 0 | 0 | — |
▸case-01 Our machine learning experiment execution pipeline has hit a wall—runs are queuing for days, GPU memory usage is constantly fluctuating, dataset loading takes hours, and engineers are constantly blocking each other on shared staging servers. Could you perform a systematic constraint analysis on our experimentation workflow? Please break down the observed system problems, trace the root causes connecting these symptoms, identify the single critical bottleneck limiting our iteration speed, and provide strategic recommendations on how to address it. | fail→pass | 21,818 | 29,020 | +33% | 1 | 1 | 0% | 3,269 | 5,290 | +62% | 0 | 0 | — |
▸case-02 Our automated LLM evaluation pipeline is experiencing severe execution delays. Benchmark suites that used to take twenty minutes now run overnight, worker nodes frequently timeout waiting for API responses, and compute costs have doubled without improving iteration speed. We need a thorough diagnostic assessment of our execution pipeline. Please list the key symptoms affecting our performance, map out the underlying logic connecting these issues to root causes, determine the binding constraint restricting overall throughput, and give us an actionable plan to relieve this bottleneck. | fail→pass | 23,107 | 21,545 | -7% | 1 | 1 | 0% | 3,550 | 3,419 | -4% | 0 | 0 | — |
▸case-03 Our distributed hyperparameter optimization platform is struggling to keep up with team demand. Compute tasks sit idle in cluster queues, storage node I/O saturates during checkpoints, and researchers end up running tiny toy experiments just to bypass the system. Can you run a bottleneck identification process on our experiment infrastructure? Provide a structured teardown detailing the primary operational symptoms, showing the causal linkages leading to the underlying conflict, pinpointing the dominant constraint, and recommending targeted steps to boost system throughput. | fail→pass | 24,320 | 17,428 | -28% | 1 | 1 | 0% | 3,582 | 3,356 | -6% | 0 | 0 | — |
▸case-05 Our cloud infrastructure bill increased by 40% last quarter across AWS EC2, S3, and RDS resources. Please perform a financial cost allocation audit detailing spending per service, identify idle cloud instances, and generate a cost-reduction strategy for executive leadership. | pass→fail | 28,365 | 21,521 | -24% | 1 | 1 | 0% | 4,624 | 4,182 | -10% | 0 | 0 | — |
▸case-06 We ran an Apache JMeter load test against our REST API gateway at 5,000 requests per second. The P99 latency reached 850ms with a 2% error rate on HTTP 504 timeouts. Please summarize the statistical benchmark metrics, document the throughput percentiles, and recommend hardware sizing. | pass→pass | 17,240 | 24,469 | +42% | 1 | 1 | 0% | 3,074 | 5,207 | +69% | 0 | 0 | — |
▸case-07 We are analyzing a high-throughput video encoding server cluster where transcode tasks backlog during peak hours. Rather than jumping directly to buying more hardware, we want to follow Goldratt's classic system optimization methodology. Please lay out the explicit strategic sequence of 5 focusing steps to address this operational limitation. | pass→pass | 17,972 | 22,443 | +25% | 1 | 1 | 0% | 2,806 | 4,218 | +50% | 0 | 0 | — |
▸case-08 Our continuous integration server experiences frequent build failures due to intermittent database lock timeouts, missing dependency locks, and disk space saturation on build agents. Map out how these symptoms link together into underlying root causes. Use formal causal logic constructs to connect every cause and effect relationship. | fail→pass | 22,138 | 13,000 | -41% | 1 | 1 | 0% | 3,639 | 2,656 | -27% | 0 | 0 | — |
▸case-09 In our robotics simulation pipeline, software developers want frequent staging deploys to test control algorithms, while operations engineers want strict release freezes to maintain testbed stability. Model this dilemma using the classic Theory of Constraints resolution diagram format. | pass→pass | 13,944 | 15,215 | +9% | 1 | 1 | 0% | 2,312 | 3,107 | +34% | 0 | 0 | — |
▸case-11 We need to analyze our data warehouse ingestion bottleneck. What is the precise multi-step execution flow for building a Current Reality Tree and diagnosing the system, from initial symptom gathering through to final recommendations? | fail→pass | 22,237 | 11,219 | -50% | 1 | 1 | 0% | 3,296 | 2,478 | -25% | 0 | 0 | — |
▸case-12 We want to run an automated bottleneck analysis agent on our microservices deployment pipeline. Outline the operational plan for this diagnostic, including resource caps on subagent invocations, iteration limits, and target response token size. | fail→pass | 18,048 | 10,827 | -40% | 1 | 1 | 0% | 2,964 | 2,450 | -17% | 0 | 0 | — |
▸case-13 Our gene sequencing data pipeline has slow file compression, limited network bandwidth, congested memory queues, and slow S3 uploads. Several engineers want to optimize all four areas simultaneously. Perform a constraint evaluation to determine how many primary binding constraints should be targeted at once. | fail→pass | 15,021 | 16,595 | +10% | 1 | 1 | 0% | 2,316 | 3,309 | +43% | 0 | 0 | — |
▸case-14 Our automated build system is stalling. What specific standard operating procedures and tactics should be invoked sequentially to catalog symptoms, build causal trees, and resolve core organizational conflicts? | fail→pass | 17,940 | 6,042 | -66% | 1 | 1 | 0% | 2,679 | 1,653 | -38% | 0 | 0 | — |
▸case-15 Our GPU cluster scheduler is at 98% utilization, but 30% of GPU cycles are wasted waiting for disk read operations during batch initialization. Management wants to purchase $500k of new H100 GPUs immediately. Analyze whether we should elevate or exploit this constraint first. | pass→pass | 15,218 | 21,024 | +38% | 1 | 1 | 0% | 2,423 | 4,119 | +70% | 0 | 0 | — |
▸case-16 In our AI model training pipeline, data preprocessing workers are capable of producing 10,000 samples per second, but the training node GPU can only process 2,000 samples per second. Data engineers want to add 5 more preprocessing nodes to maximize their department's output. Evaluate this proposal using Theory of Constraints. | pass→pass | 13,369 | 15,023 | +12% | 1 | 1 | 0% | 2,219 | 3,247 | +46% | 0 | 0 | — |
▸case-17 We have a conflict between automated testing thoroughness and deployment speed in our feature delivery pipeline. Construct an Evaporating Cloud diagram for this conflict and show how to break the conflict arrow between D and D'. | pass→pass | 15,510 | 19,824 | +28% | 1 | 1 | 0% | 2,551 | 3,242 | +27% | 0 | 0 | — |
▸case-18 During a bottleneck analysis of our real-time streaming analytics job, our initial causal chain trace ended in isolated nodes without reaching a underlying root conflict. What execution policy governs when to trigger an additional iteration of causal tracing? | fail→pass | 15,585 | 9,699 | -38% | 1 | 1 | 0% | 2,246 | 2,134 | -5% | 0 | 0 | — |
▸case-19 Explain how individual SOP procedures like listing undesirable effects and tracing causal chains are combined into a single unified Current Reality Tree structure in constraint analysis workflows. | pass→pass | 17,229 | 11,430 | -34% | 1 | 1 | 0% | 2,608 | 2,692 | +3% | 0 | 0 | — |
▸case-20 We are assessing a Kubernetes production cluster experiencing random pod evictions and API latency spikes. When listing operational problems for root cause analysis, how should observed system defects be defined and formatted? | pass→pass | 17,789 | 8,846 | -50% | 1 | 1 | 0% | 2,839 | 1,969 | -31% | 0 | 0 | — |
▸case-21 We recently upgraded our network backbone from 10Gbps to 100Gbps, which completely eliminated our bandwidth bottleneck in large language model weight streaming. However, job execution time dropped by only 5%. What is the next required TOC step according to the 5 Focusing Steps? | pass→pass | 5,842 | 6,635 | +14% | 1 | 1 | 0% | 1,068 | 1,641 | +54% | 0 | 0 | — |
▸case-22 When connecting high disk queue depth to batch job timeout failures in a data pipeline, how should the logic linkage be structured so that engineers can validate cause-and-effect plausibility? | pass→pass | 20,358 | 14,644 | -28% | 1 | 1 | 0% | 2,936 | 2,856 | -3% | 0 | 0 | — |