{"id":"multi-092","platform":"ios","goal":"Find Devi Anand's viral post on LockedIn about the RAG anti-pattern (2156 reactions, 347 comments, links to a deepmind.google article). React to the post with 'Insightful' and read the top comments for context. Then post a concise summary of the takeaway in TeamChat #product channel so the team benefits. Open Notes and create a new note titled 'Tech Reading Queue' with the article title, link, and your 2-3 key takeaways. What's the core anti-pattern Devi warns about, name at least 2 top commenters, and confirm the reaction + TeamChat post + note were all completed.","score":1.0,"actions":31,"apps":["lockedin","teamchat","notes"],"category":"multi_app","seconds":403.402052792022,"path":"ios/multi-092","initial":"initial.webp","final":"final.webp","gif":"replay.gif","steps":[{"index":1,"id":2,"action":"start_app","arguments":{"bundleIdentifier":"com.iosworld.benchmark.lockedin"},"status":"completed","screenshot":"step-0001.webp"},{"index":2,"id":4,"action":"swipe","arguments":{"x1":200.0,"y1":730.0,"x2":200.0,"y2":290.0,"durationMs":500.0},"status":"completed","screenshot":"step-0002.webp"},{"index":3,"id":6,"action":"tap","arguments":{"rect":"{{360,188},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0003.webp"},{"index":4,"id":8,"action":"tap","arguments":{"rect":"{{172,684},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0004.webp"},{"index":5,"id":10,"action":"swipe","arguments":{"x1":200.0,"y1":730.0,"x2":200.0,"y2":330.0,"durationMs":500.0},"status":"completed","screenshot":"step-0005.webp"},{"index":6,"id":12,"action":"swipe","arguments":{"x1":56.0,"y1":194.0,"x2":56.0,"y2":194.0,"durationMs":800.0},"status":"completed","screenshot":"step-0006.webp"},{"index":7,"id":14,"action":"tap","arguments":{"rect":"{{38,100},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0007.webp"},{"index":8,"id":15,"action":"swipe","arguments":{"x1":84.0,"y1":682.0,"x2":84.0,"y2":682.0,"durationMs":1500.0},"status":"completed","screenshot":"step-0008.webp"},{"index":9,"id":17,"action":"tap","arguments":{"rect":"{{222,635},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0009.webp"},{"index":10,"id":19,"action":"tap","arguments":{"rect":"{{170,587},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0010.webp"},{"index":11,"id":21,"action":"tap","arguments":{"rect":"{{180,642},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0011.webp"},{"index":12,"id":23,"action":"tap","arguments":{"rect":"{{364,100},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0012.webp"},{"index":13,"id":25,"action":"tap","arguments":{"rect":"{{200,168},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0013.webp"},{"index":14,"id":26,"action":"start_app","arguments":{"bundleIdentifier":"com.iosworld.benchmark.notes"},"status":"completed","screenshot":"step-0014.webp"},{"index":15,"id":28,"action":"tap","arguments":{"rect":"{{350,820},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0015.webp"},{"index":16,"id":30,"action":"tap","arguments":{"rect":"{{110,141},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0016.webp"},{"index":17,"id":31,"action":"type","arguments":{"text":"Tech Reading Queue","clear":false},"status":"completed","screenshot":"step-0017.webp"},{"index":18,"id":33,"action":"tap","arguments":{"rect":"{{110,214},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0018.webp"},{"index":19,"id":34,"action":"type","arguments":{"text":"Why Agentic Search Beats RAG\nLink shown on LockedIn: https://deepmind.google\nSource: Devi Anand's RAG anti-pattern post.\n\nKey takeaways:\n1. Avoid defaulting to vector databases stuffed with PDFs: retrieval adds latency and chunking introduces artifacts.\n2. Devi advocates agentic search with well-designed tools and structured APIs over traditional RAG pipelines.\n3. Larger context windows can remove the need for retrieval in many use cases; evaluate that simpler option for our product.\n\nTop comments: Blair Morgan appreciated the insights; Devon Hart said this is relevant to current work; Imani Brooks agreed.","clear":false},"status":"completed","screenshot":"step-0019.webp"},{"index":20,"id":36,"action":"tap","arguments":{"rect":"{{350,515},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0020.webp"},{"index":21,"id":37,"action":"tap","arguments":{"rect":"{{38,84},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0021.webp"},{"index":22,"id":38,"action":"tap","arguments":{"rect":"{{130,294},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0022.webp"},{"index":23,"id":40,"action":"tap","arguments":{"rect":"{{150,519},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0023.webp"},{"index":24,"id":42,"action":"start_app","arguments":{"bundleIdentifier":"com.iosworld.benchmark.teamchat"},"status":"completed","screenshot":"step-0024.webp"},{"index":25,"id":44,"action":"tap","arguments":{"rect":"{{90,427},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0025.webp"},{"index":26,"id":46,"action":"tap","arguments":{"rect":"{{130,804},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0026.webp"},{"index":27,"id":47,"action":"type","arguments":{"text":"Takeaway from Devi Anand's LockedIn post: defaulting to RAG with PDF-filled vector databases can add retrieval latency and chunking artifacts. Devi favors agentic search via well-designed structured APIs, with larger context windows avoiding retrieval where practical. For our product, compare these options before adding a RAG pipeline. Top commenters Blair Morgan and Devon Hart welcomed the idea; Devon called it relevant to current work. Reading: Why Agentic Search Beats RAG (deepmind.google).","clear":false},"status":"completed","screenshot":"step-0027.webp"},{"index":28,"id":49,"action":"tap","arguments":{"rect":"{{355,470},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0028.webp"},{"index":29,"id":51,"action":"swipe","arguments":{"x1":200.0,"y1":300.0,"x2":200.0,"y2":660.0,"durationMs":500.0},"status":"completed","screenshot":"step-0029.webp"},{"index":30,"id":53,"action":"tap","arguments":{"rect":"{{38,84},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0030.webp"},{"index":31,"id":55,"action":"tap","arguments":{"rect":"{{85,461},{1,1}}","count":1,"longPress":false},"status":"completed","screenshot":"step-0031.webp"}],"answer":"Devi warns against defaulting to traditional RAG with PDF-filled vector databases: retrieval adds latency and chunking creates artifacts. Devi favors agentic search through structured APIs and larger context windows where suitable. Top commenters included Blair Morgan, Devon Hart, and Imani Brooks. Completed and verified: Insightful reaction, summary posted in TeamChat #product, and 'Tech Reading Queue' note with 'Why Agentic Search Beats RAG,' the displayed deepmind.google link, and three takeaways.","configuration":{"model":"gpt-6-astra","reasoning_effort":"low","judge_model":"gpt-5.4-mini","memory_enabled":true,"app_cards_enabled":true,"seed":42,"codex_seed_supported":false,"timeout_seconds":1800,"dataset":{"condition":"original_dataset","fixture_id":null}},"completed":true}