{"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":4,"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":"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":"anthropic-building-effective-agents-2024","title":"Building effective agents","year":2024,"studyKind":"vendor_writeup","classIds":["orchestrator","coding","research"],"failureModeIds":["runaway-spend","instruction-drift"],"lesson":"The post separates workflows, where the path is orchestrated in code, from agents, where the model decides the path. It says agentic systems often trade latency and cost for task performance, and that the successful implementations they saw used simple composable patterns. A note on the fetched page says much of the tooling landscape described has changed since December 2024.","sourceUrl":"https://www.anthropic.com/engineering/building-effective-agents","asOf":"2026-10-03","notes":"Fetched page says Published Dec 19, 2024. This is a design note, not an incident with a measured outage."},{"id":"anthropic-multi-agent-research-2025","title":"How we built our multi-agent research system","year":2025,"studyKind":"vendor_writeup","classIds":["research","orchestrator"],"failureModeIds":["instruction-drift","tool-loop","inter-agent-misalignment"],"lesson":"The post describes an orchestrator-worker research system. Watching traces showed agents continuing after they already had enough results, using overly verbose search queries, or selecting the wrong tool. It also says parallel tool use cut research time by up to 90% for complex queries in their system. That 90% figure is Anthropic's report about their Research feature, not a Depot measurement.","sourceUrl":"https://www.anthropic.com/engineering/built-multi-agent-research-system","asOf":"2026-10-03","notes":"Fetched page says Published Jun 13, 2025."},{"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."}]}