{"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":11,"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":"gaia-2023","title":"GAIA: a benchmark for General AI Assistants","year":2023,"studyKind":"capability_ceiling","classIds":["research","personal-assistant"],"failureModeIds":["weak-verification"],"lesson":"The abstract says GAIA questions need reasoning, multi-modal handling, web browsing, and tool use, and that they are conceptually simple for people. It reports human respondents at 92% and GPT-4 with plugins at 15%, on a set of 466 questions. That gap is a ceiling for a general assistant, not an incident at a company.","sourceUrl":"https://arxiv.org/abs/2311.12983","asOf":"2026-10-03","notes":"Fetched the arXiv abstract (submitted 21 Nov 2023). Answers for 300 questions were held for a leaderboard, so a public question file is not the full scored set."},{"id":"webarena-2024","title":"WebArena: A Realistic Web Environment for Building Autonomous Agents","year":2024,"studyKind":"capability_ceiling","classIds":["browser"],"failureModeIds":["weak-verification","instruction-drift"],"lesson":"The abstract describes functional sites for e-commerce, forums, collaborative software development, and content management, plus long-horizon tasks. It reports a best GPT-4-based agent at 14.41% end-to-end success and human performance at 78.24%. Use it as a web-agent ceiling, not as evidence about a named vendor's production site.","sourceUrl":"https://arxiv.org/abs/2307.13854","asOf":"2026-10-03","notes":"Fetched arXiv v4, last revised 16 Apr 2024. Submitted 25 Jul 2023."},{"id":"osworld-2024","title":"OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments","year":2024,"studyKind":"capability_ceiling","classIds":["computer-use"],"failureModeIds":["gui-grounding-miss","weak-verification"],"lesson":"The abstract describes 369 real computer tasks across operating systems and apps, with execution-based checks. It says humans accomplish over 72.36% and the best model 12.24%, and that models struggle with GUI grounding and operational knowledge. This is a computer-use ceiling from the paper, not a production outage.","sourceUrl":"https://arxiv.org/abs/2404.07972","asOf":"2026-10-03","notes":"Fetched arXiv v2, last revised 30 May 2024. Later site notes about OSWorld-Verified are a separate study."},{"id":"tau-bench-2024","title":"τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","year":2024,"studyKind":"capability_ceiling","classIds":["support","voice"],"failureModeIds":["non-determinism","policy-conflict","weak-verification"],"lesson":"The abstract says τ-bench checks whether an agent follows domain rules while talking to a simulated user, and grades the database at the end. It reports that even a function-calling agent such as gpt-4o succeeded on under 50% of tasks and was inconsistent, with pass^8 under 25% in the retail domain. One green trial is not reliability.","sourceUrl":"https://arxiv.org/abs/2406.12045","asOf":"2026-10-03","notes":"Fetched the arXiv abstract (submitted 17 Jun 2024). Capability ceiling for policy-following tool use, not a named company's outage."},{"id":"the-agent-company-2025","title":"TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks","year":2025,"studyKind":"capability_ceiling","classIds":["coding","browser","research","orchestrator"],"failureModeIds":["weak-verification"],"lesson":"The abstract describes a simulated software company where agents browse, write code, run programs, and communicate with coworkers. It says the most competitive agent they tested completed 30% of tasks autonomously. Read that as a ceiling inside their simulation, not as a measurement of any employer's production team.","sourceUrl":"https://arxiv.org/abs/2412.14161","asOf":"2026-10-03","notes":"Fetched arXiv v3, last revised 10 Sep 2025. Submitted 18 Dec 2024."},{"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":"appworld-2024","title":"AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents","year":2024,"studyKind":"capability_ceiling","classIds":["coding","personal-assistant"],"failureModeIds":["collateral-side-effects","weak-verification"],"lesson":"The abstract describes 9 apps and 457 APIs, and 750 tasks whose checks allow more than one solution while also flagging unexpected changes. It says GPT-4o solved about 49% of normal tasks and about 30% of challenge tasks. A task that reaches the goal and also mutates something else is still a failed check in this design.","sourceUrl":"https://arxiv.org/abs/2407.18901","asOf":"2026-10-03","notes":"Fetched the arXiv abstract. Capability ceiling, not a company incident."},{"id":"workarena-2024","title":"WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?","year":2024,"studyKind":"capability_ceiling","classIds":["browser","support"],"failureModeIds":["weak-verification"],"lesson":"The abstract introduces WorkArena as 33 tasks on ServiceNow and BrowserGym as the environment. It says agents showed promise and that a considerable gap remained before full task automation, with a disparity between open and closed models. No success percentage is stored because the abstract did not state one.","sourceUrl":"https://arxiv.org/abs/2403.07718","asOf":"2026-10-03","notes":"Fetched the arXiv abstract (submitted 12 Mar 2024). Enterprise knowledge-work ceiling, not a ServiceNow incident."}]}