{"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":2,"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":"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":"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."}]}