{"asOf":"2026-10-03","doesNotChangeDepotPicks":true,"doesNotChangePriceSort":true,"doesNotChangeFreeIndex":true,"note":"Classification and failure modes are mechanisms. Case studies are included only when the source URL was fetched on 2026-10-03. A capability ceiling is not a company incident. Vendor figures are labeled as that vendor's report.","count":7,"catalogCount":27,"refused":["Replit agent database story (July 2025): The Register URL returned HTTP 403 and no first-party engineering postmortem was fetched. Not indexed.","CanLII HTML for 2024 BCCRT 149 returned a client-side block. The Civil Resolution Tribunal document was fetched instead and is the indexed source.","Cursor agent best-practices post loaded and is a product guide, not a postmortem. Not indexed as an incident. The University of Chicago study it mentions was not fetched.","Cognition 'Introducing Devin' (published 2024-03-12) duplicates the SWE-bench technical report. The report is indexed; the launch post is not a second study.","Anthropic many-shot jailbreaking research page loaded. It is an attack writeup, so it is not indexed and no procedure is stored.","OWASP LLM01:2025 was fetched and is cited on the prompt-injection failure mode only. Attack examples from that page are not copied into a case study.","Calder Lab and similar unsourced production-metric blogs were not fetched and are not indexed.","Google Research abstract for Data Cascades in High-Stakes AI loaded on 2026-10-03 (the abstract says 92% prevalence). Not indexed for data-etl: it is an interview study of ML data practice, not a pipeline job failure. The indexed data-etl study is the dbt Cloud status page."],"studies":[{"id":"swe-bench-2023","title":"SWE-bench: Can Language Models Resolve Real-World GitHub Issues?","year":2023,"studyKind":"capability_ceiling","classIds":["coding","eval-judge"],"failureModeIds":["weak-verification","context-overflow"],"lesson":"The abstract describes 2,294 GitHub issues from 12 Python repositories. Resolving them often means coordinating edits across functions, classes, and files, and interacting with an execution environment. The abstract says Claude 2 solved 1.96% of the issues. Treat that figure as the paper's reported ceiling at publication, not a live Depot score.","sourceUrl":"https://arxiv.org/abs/2310.06770","asOf":"2026-10-03","notes":"Fetched the arXiv abstract (v3, last revised 11 Nov 2024). Not a company incident. Depot did not re-run the harness."},{"id":"swe-bench-verified-2024","title":"Introducing SWE-bench Verified","year":2024,"studyKind":"vendor_writeup","classIds":["coding","eval-judge"],"failureModeIds":["eval-contamination","benchmark-protocol-mismatch","weak-verification"],"lesson":"OpenAI's page says the original SWE-bench can grade a valid patch as wrong when unit tests are overly specific, the issue text is underspecified, or the environment fails to set up. They released a 500-sample human-validated subset, with difficulty slices of 196 tasks they mark as under 15 minutes and 45 they mark as over an hour. Cite the subset name with any score.","sourceUrl":"https://openai.com/index/introducing-swe-bench-verified/","asOf":"2026-10-03","notes":"Page date on the fetch: August 13, 2024. Model score tables on that page were not copied here."},{"id":"cognition-swe-bench-report-2024","title":"Cognition SWE-bench technical report","year":2024,"studyKind":"vendor_writeup","classIds":["coding","eval-judge"],"failureModeIds":["benchmark-protocol-mismatch"],"lesson":"Cognition's report says Devin resolved 13.86% of issues on a random 25% subset, unassisted, against a previous unassisted baseline of 1.96%, and that the best previous model resolved 4.80% when given the files to edit. The page itself distinguishes assisted from unassisted. Do not cite those percentages as the same protocol.","sourceUrl":"https://www.cognition.ai/blog/swe-bench-technical-report","asOf":"2026-10-03","notes":"Vendor self-report. article:published_time on the fetched page is 2024-03-15. Depot did not re-run the harness. The launch post Introducing Devin was not indexed separately."},{"id":"osworld-verified-site-2025","title":"OSWorld project site: verified upgrade","year":2025,"studyKind":"capability_ceiling","classIds":["computer-use","eval-judge"],"failureModeIds":["benchmark-protocol-mismatch","eval-contamination"],"lesson":"The project site says that on 2025-07-28 OSWorld was enhanced as OSWorld-Verified, including fixed community-reported examples and updated benchmark results, and that new runs should be compared with those new results. A 2026-06-26 line says OSWorld 2.0 is available. A score from the 2024 paper is not the same number as a score on the later set.","sourceUrl":"https://os-world.github.io/","asOf":"2026-10-03","notes":"Fetched the project homepage. What the 2.0 task set contains was not copied because the homepage line only says it is available."},{"id":"agentbench-2024","title":"AgentBench: Evaluating LLMs as Agents","year":2024,"studyKind":"capability_ceiling","classIds":["coding","eval-judge"],"failureModeIds":["instruction-drift","hallucinated-tool-args","weak-verification"],"lesson":"The abstract says AgentBench uses 8 environments and that the main obstacles they identified were poor long-term reasoning, decision-making, and instruction following. A coding or tool agent that cannot stay on the instruction over many steps fails this kind of eval even if a single call looks fluent. The abstract's model count did not render as a number on the fetched page, so none is stored.","sourceUrl":"https://arxiv.org/abs/2308.03688","asOf":"2026-10-03","notes":"Fetched the arXiv abstract. Comment on the page: published in ICLR 2024."},{"id":"mast-multi-agent-failures-2025","title":"Why Do Multi-Agent LLM Systems Fail?","year":2025,"studyKind":"mechanism_study","classIds":["orchestrator","eval-judge"],"failureModeIds":["inter-agent-misalignment","weak-verification"],"lesson":"The abstract says performance gains of multi-agent systems on popular benchmarks are often minimal. Their taxonomy, built from 150 traces with annotator kappa of 0.88, groups failures into system design, inter-agent misalignment, and task verification, and identifies 14 modes. The larger MAST-Data collection is described as 1600+ annotated traces. This is a failure taxonomy, not a single production outage.","sourceUrl":"https://arxiv.org/abs/2503.13657","asOf":"2026-10-03","notes":"Fetched the arXiv abstract (submitted 2025). The 14 mode names are not copied because the abstract did not list them."},{"id":"langsmith-cert-incident-2025","title":"LangSmith incident on May 1, 2025","year":2025,"studyKind":"production_incident","classIds":["eval-judge","scheduled-ops"],"failureModeIds":["observability-gap"],"lesson":"LangChain's incident note says the US LangSmith API had an elevated error rate for 28 minutes on May 1, 2025, from 14:35 to 15:03 UTC, and that about 55% of API requests failed, including run ingestion. The cause they describe is a leftover DNS record that stopped certificate renewal until the certificate expired. Agent traces can disappear because the trace host's certificate failed, not because the agent reasoned badly.","sourceUrl":"https://www.langchain.com/blog/langsmith-incident-on-may-1-2025","asOf":"2026-10-03","notes":"Fetched LangChain's own incident writeup. This is an observability outage, not a model-behavior incident."}]}