[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-e88d84a31fb11cf1-internal-ai-adoption-the-rise-of-agentic-workflows-summary":3,"summaries-facets-categories":147,"summary-related-e88d84a31fb11cf1-internal-ai-adoption-the-rise-of-agentic-workflows-summary":5651},{"id":4,"title":5,"ai":6,"body":13,"categories":97,"created_at":99,"date_modified":99,"description":90,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":102,"navigation":129,"path":130,"published_at":131,"question":99,"scraped_at":131,"seo":132,"sitemap":133,"source_id":134,"source_name":135,"source_type":136,"source_url":137,"stem":138,"tags":139,"thumbnail_url":99,"tldr":144,"tweet":99,"unknown_tags":145,"__hash__":146},"summaries\u002Fsummaries\u002Fe88d84a31fb11cf1-internal-ai-adoption-the-rise-of-agentic-workflows-summary.md","Internal AI Adoption & The Rise of Agentic Workflows",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",9867,822,3719,0.00369975,{"type":14,"value":15,"toc":89},"minimark",[16,21,25,29,32,36,39,62,66,69],[17,18,20],"h2",{"id":19},"internal-adoption-as-a-leading-indicator","Internal Adoption as a Leading Indicator",[22,23,24],"p",{},"OpenAI's internal data reveals a significant shift in how AI is utilized within the organization. Between November 2025 and June 2026, median Codex output tokens increased by 56x in Research, 32x in Customer Support, 27x in Engineering, and 13x in Legal. This surge suggests that the most effective path to production adoption involves supporting persistent workflows, human-in-the-loop review cycles, and specialized tooling rather than relying on general-purpose chat interfaces.",[17,26,28],{"id":27},"the-shift-to-long-horizon-agent-infrastructure","The Shift to Long-Horizon Agent Infrastructure",[22,30,31],{},"Engineering efforts are increasingly focused on agentic persistence rather than just minimizing interactive latency. New infrastructure providers like Sail ($80M raised) and Hyperagent are optimizing for workloads that run for days or weeks, providing dedicated cloud environments for browser and code execution. This aligns with a broader industry trend: using general-purpose chat for one-off answers and specialized, persistent agents for repeatable, multi-step tasks.",[17,33,35],{"id":34},"evolving-evaluation-and-synthetic-data","Evolving Evaluation and Synthetic Data",[22,37,38],{},"Public benchmarks are increasingly viewed as compromised due to data leakage (models retrieving solutions from the internet or git history). Research is shifting toward:",[40,41,42,50,56],"ul",{},[43,44,45,49],"li",{},[46,47,48],"strong",{},"No-internet evaluation harnesses:"," Stricter environments to prevent benchmark hacking.",[43,51,52,55],{},[46,53,54],{},"Autodata loops:"," Using agentic loops to generate, analyze, and meta-optimize synthetic training data, which has improved pass rates in legal and math tasks from 62.1% to 79.6%.",[43,57,58,61],{},[46,59,60],{},"Test-time compute via curation:"," Techniques like those from Datology demonstrate that data curation can improve model efficiency by 35x by inducing concision without sacrificing performance.",[17,63,65],{"id":64},"open-model-ecosystem","Open Model Ecosystem",[22,67,68],{},"Open models continue to close the gap with frontier labs. Notable releases include:",[40,70,71,77,83],{},[43,72,73,76],{},[46,74,75],{},"GLM-5.2 Max:"," Reached 1595 on Code Arena (Frontend) and demonstrated high agentic reliability with zero failed runs across 84 tests.",[43,78,79,82],{},[46,80,81],{},"Ornith-1.0:"," A family of models (9B to 397B MoE) utilizing self-improving RL to optimize task-specific scaffolds.",[43,84,85,88],{},[46,86,87],{},"Liquid AI LFM2.5-230M:"," An ultra-small model optimized for low-latency tool use, achieving ~1400 tok\u002Fs locally via WebGPU.",{"title":90,"searchDepth":91,"depth":91,"links":92},"",2,[93,94,95,96],{"id":19,"depth":91,"text":20},{"id":27,"depth":91,"text":28},{"id":34,"depth":91,"text":35},{"id":64,"depth":91,"text":65},[98],"Agents & Orchestration",null,"md",false,{"content_references":103,"triage":123},[104,109,112,115,117,119],{"type":105,"title":106,"author":107,"context":108},"tool","GLM-5.2","Z.ai","mentioned",{"type":105,"title":110,"author":111,"context":108},"Ornith-1.0","DeepReinforce-AI",{"type":105,"title":113,"author":114,"context":108},"LFM2.5-230M","Liquid AI",{"type":105,"title":116,"context":108},"Sail",{"type":105,"title":118,"context":108},"Hyperagent",{"type":105,"title":120,"author":121,"url":122,"context":108},"Unlimited-OCR","Baidu","https:\u002F\u002Fgithub.com\u002Fbaidu\u002FUnlimited-OCR",{"relevance":124,"novelty":125,"quality":125,"actionability":126,"composite":127,"reasoning":128},5,4,3,4.15,"Category: Agents & Orchestration. The article discusses the shift towards agentic workflows and persistent infrastructure, which directly addresses the audience's interest in architectures and tool use for production AI systems. It provides insights into internal adoption metrics and evolving evaluation methods, making it relevant and somewhat actionable, though it lacks specific step-by-step guidance.",true,"\u002Fsummaries\u002Fe88d84a31fb11cf1-internal-ai-adoption-the-rise-of-agentic-workflows-summary","2026-06-29 14:32:24",{"title":5,"description":90},{"loc":130},"e88d84a31fb11cf1","Latent Space (Newsletter)","article","https:\u002F\u002Fwww.latent.space\u002Fp\u002Fainews-openai-reports-median-internal","summaries\u002Fe88d84a31fb11cf1-internal-ai-adoption-the-rise-of-agentic-workflows-summary",[140,141,142,143],"llm","agents","inference","benchmarks","OpenAI reports massive internal token growth across all departments, signaling that agentic workflows—supported by review loops and persistent infrastructure—are moving from experimental to core production 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In these domains, the model's underlying reasoning capabilities appear to override the stylistic constraints imposed by personality prompts.",[17,5677,5679],{"id":5678},"when-personality-becomes-a-performance-bottleneck","When Personality Becomes a Performance Bottleneck",[22,5681,5682,5683,5686,5687,5690],{},"Conversely, in ",[46,5684,5685],{},"open-ended research collaboration"," and ",[46,5688,5689],{},"competitive bargaining",", personality manipulation is a critical performance variable. When agents are prompted to be less agreeable in these contexts, performance degrades substantially. This suggests that for tasks requiring high levels of social coordination, consensus-building, or negotiation, the \"personality\" of the agent is not merely cosmetic but a functional component of the system's success.",[17,5692,5694],{"id":5693},"design-implications-for-multi-agent-systems","Design Implications for Multi-Agent Systems",[22,5696,5697],{},"These findings suggest that developers should not treat personality as a global configuration for multi-agent systems. Instead, personality-based prompting should be:",[40,5699,5700,5706,5712],{},[43,5701,5702,5705],{},[46,5703,5704],{},"Context-Aware:"," Apply cooperative personality traits to agents handling negotiation or collaborative brainstorming.",[43,5707,5708,5711],{},[46,5709,5710],{},"Task-Isolated:"," Recognize that for technical, objective-driven tasks (like coding or data processing), personality constraints may introduce unnecessary communication overhead without providing any functional benefit.",[43,5713,5714,5717],{},[46,5715,5716],{},"Evaluated via Domain:"," Performance benchmarks for agentic systems must include diverse task types to ensure that stylistic prompting does not inadvertently sabotage collaborative workflows.",{"title":90,"searchDepth":91,"depth":91,"links":5719},[5720,5721,5722],{"id":5664,"depth":91,"text":5665},{"id":5678,"depth":91,"text":5679},{"id":5693,"depth":91,"text":5694},[98],{"content_references":5725,"triage":5732},[5726],{"type":5727,"title":5728,"author":5729,"url":5730,"context":5731},"paper","When Does Personality Composition Matter for Multi-Agent LLM Teams?","Aryan Keluskar, Amrita Bhattacharjee, Huan Liu","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.27443","reviewed",{"relevance":125,"novelty":126,"quality":125,"actionability":126,"composite":5733,"reasoning":5734},3.6,"Category: Agents & Orchestration. The article discusses the impact of personality prompting on LLM agents, which directly relates to architectures and multi-agent systems. It provides insights into how personality affects performance in different task contexts, addressing a specific audience pain point regarding agent behavior in production systems.","\u002Fsummaries\u002F357649c737150a2a-personality-prompting-in-multi-agent-teams-task-de-summary","2026-06-29 14:33:21",{"title":5654,"description":90},{"loc":5735},"357649c737150a2a","arXiv cs.AI","summaries\u002F357649c737150a2a-personality-prompting-in-multi-agent-teams-task-de-summary",[141,140,5743,143],"architectures","Personality manipulation in LLM agents significantly alters communication style but only degrades task performance in open-ended or collaborative domains, while remaining largely neutral in structured coding tasks.",[],"khba6PHL-_BeKYoJyqhesmkiK9D7MqAS_PlkEMURIFk",{"id":5748,"title":5749,"ai":5750,"body":5755,"categories":5831,"created_at":99,"date_modified":99,"description":90,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":5832,"navigation":129,"path":5853,"published_at":5854,"question":99,"scraped_at":5854,"seo":5855,"sitemap":5856,"source_id":5857,"source_name":5858,"source_type":136,"source_url":5859,"stem":5860,"tags":5861,"thumbnail_url":99,"tldr":5863,"tweet":99,"unknown_tags":5864,"__hash__":5865},"summaries\u002Fsummaries\u002F1cd4894311055819-parallelkernelbench-frontier-llms-struggle-with-mu-summary.md","ParallelKernelBench: Frontier LLMs Struggle with Multi-GPU Kernels",{"provider":7,"model":8,"input_tokens":5751,"output_tokens":5752,"processing_time_ms":5753,"cost_usd":5754},9003,895,4216,0.00359325,{"type":14,"value":5756,"toc":5826},[5757,5761,5764,5768,5771,5791,5795,5798,5823],[17,5758,5760],{"id":5759},"the-shift-to-multi-gpu-complexity","The Shift to Multi-GPU Complexity",[22,5762,5763],{},"While LLMs have demonstrated proficiency in writing single-GPU kernels, production AI systems are increasingly bottlenecked by inter-GPU communication rather than local compute. ParallelKernelBench (PKB) introduces a benchmark suite of 87 problems derived from real-world codebases (e.g., Megatron-LM, DeepSpeed, NeMo-RL) to evaluate how well frontier models can replace standard PyTorch + NCCL implementations with custom CUDA kernels that utilize direct NVLink communication.",[17,5765,5767],{"id":5766},"performance-and-failure-modes","Performance and Failure Modes",[22,5769,5770],{},"Frontier models currently struggle with this task. In zero-shot settings, the best models solve fewer than 35% of problems correctly, and even fewer outperform the naive PyTorch + NCCL baseline. Key findings include:",[40,5772,5773,5779,5785],{},[43,5774,5775,5778],{},[46,5776,5777],{},"Reasoning Gaps:"," Unlike single-GPU tasks where models often fail at syntax, multi-GPU failures frequently involve valid code that produces incorrect results or deadlocks due to poor rank coordination and data partitioning.",[43,5780,5781,5784],{},[46,5782,5783],{},"Limited Primitive Usage:"," Models heavily rely on basic copy engines or SM load\u002Fstore instructions, largely ignoring specialized, high-performance primitives like TMA (Tensor Memory Accelerator) and NVLS (NVIDIA Link Store).",[43,5786,5787,5790],{},[46,5788,5789],{},"Agentic Limitations:"," Wrapping models in an agentic loop (allowing for compilation, testing, and iteration) provides only modest gains. Performance typically plateaus after ~20 refinement steps, indicating that the core issue is a lack of deep reasoning regarding communication ordering and hardware-specific abstractions.",[17,5792,5794],{"id":5793},"surprising-successes-and-future-potential","Surprising Successes and Future Potential",[22,5796,5797],{},"Despite the overall low success rate, models occasionally produce kernels that outperform public references, particularly in domains with less optimized existing code. Examples include:",[40,5799,5800,5806,5812],{},[43,5801,5802,5805],{},[46,5803,5804],{},"NeMo-RL Vocab-Parallel Log-Prob:"," A fused kernel that skips standard collectives by permuting shards inline.",[43,5807,5808,5811],{},[46,5809,5810],{},"Hyena Forward Context Parallelism:"," A kernel that packs inputs into symmetric allocations to stream remote slices over NVLink.",[43,5813,5814,5817,5818,5822],{},[46,5815,5816],{},"SAM 3 Mask IoU Suppression:"," A pipeline that collapses variable-length ",[5819,5820,5821],"code",{},"all_gather"," collectives into bitpacked symmetric-memory operations.",[22,5824,5825],{},"These successes suggest that while current frontier models lack the priors for complex distributed systems, they possess the potential to optimize specialized workloads beyond standard Transformer blocks.",{"title":90,"searchDepth":91,"depth":91,"links":5827},[5828,5829,5830],{"id":5759,"depth":91,"text":5760},{"id":5766,"depth":91,"text":5767},{"id":5793,"depth":91,"text":5794},[180],{"content_references":5833,"triage":5851},[5834,5839,5842,5845,5848],{"type":5727,"title":5835,"author":5836,"url":5837,"context":5838},"ParallelKernelBench: Can LLMs Write Fast Multi-GPU Kernels?","Willy Chan, Nathan Paek, Simon Guo, Simran Arora, Daniel Y. Fu","https:\u002F\u002Fwww.alphaxiv.org\u002Fabs\u002F2606.parallel-kernel-bench","cited",{"type":105,"title":5840,"url":5841,"context":108},"Megatron-LM","https:\u002F\u002Fgithub.com\u002Fnvidia\u002Fmegatron-lm",{"type":105,"title":5843,"url":5844,"context":108},"DeepSpeed","https:\u002F\u002Fgithub.com\u002Fdeepspeedai\u002Fdeepspeed",{"type":105,"title":5846,"url":5847,"context":108},"TensorRT-LLM","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FTensorRT-LLM",{"type":105,"title":5849,"url":5850,"context":5838},"ThunderKittens 2.0","https:\u002F\u002Fhazyresearch.stanford.edu\u002Fblog\u002F2026-02-19-tk-2",{"relevance":124,"novelty":125,"quality":125,"actionability":126,"composite":127,"reasoning":5852},"Category: Models & Frontier Labs. The article provides a detailed analysis of how frontier LLMs perform in multi-GPU kernel generation, addressing a specific pain point for engineers focused on inference and performance optimization. It introduces the ParallelKernelBench benchmark suite, which is actionable for engineers looking to evaluate and improve their multi-GPU implementations.","\u002Fsummaries\u002F1cd4894311055819-parallelkernelbench-frontier-llms-struggle-with-mu-summary","2026-06-29 14:32:17",{"title":5749,"description":90},{"loc":5853},"1cd4894311055819","Together AI Blog","https:\u002F\u002Fwww.together.ai\u002Fblog\u002Fparallelkernelbench","summaries\u002F1cd4894311055819-parallelkernelbench-frontier-llms-struggle-with-mu-summary",[140,142,143,5862],"cuda","While LLMs excel at single-GPU kernel generation, they currently struggle with multi-GPU tasks where communication bottlenecks and complex rank coordination dominate performance.",[5862],"ur1KtcdjOgDKg2-T5TWAnx2os9sERzyOIsN8UeuxSKI",{"id":5867,"title":5868,"ai":5869,"body":5875,"categories":5906,"created_at":99,"date_modified":99,"description":90,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":5907,"navigation":129,"path":5912,"published_at":5913,"question":99,"scraped_at":5913,"seo":5914,"sitemap":5915,"source_id":5916,"source_name":5740,"source_type":136,"source_url":5917,"stem":5918,"tags":5919,"thumbnail_url":99,"tldr":5920,"tweet":99,"unknown_tags":5921,"__hash__":5922},"summaries\u002Fsummaries\u002Ff830c7083c5c4449-cocoda-co-evolve-dags-to-scale-tool-augmented-agen-summary.md","CoCoDA: Co-Evolve DAGs to Scale Tool-Augmented Agents",{"provider":7,"model":5870,"input_tokens":5871,"output_tokens":5872,"processing_time_ms":5873,"cost_usd":5874},"x-ai\u002Fgrok-4.1-fast",5903,1416,20966,0.00138175,{"type":14,"value":5876,"toc":5901},[5877,5881,5884,5888,5891,5895,5898],[17,5878,5880],{"id":5879},"dag-structure-enables-typed-compositional-tools","DAG Structure Enables Typed, Compositional Tools",[22,5882,5883],{},"Tool-augmented agents face scaling issues: libraries grow but fixed context budgets limit retrieval, and flat text indexing ignores code's typed, hierarchical nature. CoCoDA solves this with a single code-native Directed Acyclic Graph (DAG). Nodes represent primitive (base) or composite (higher-level) tools, storing typed signatures, descriptions, pre\u002Fpost-conditions, and worked examples. Edges define invocation dependencies, capturing reusable subroutines as composable units. This structure avoids prompt bloat by treating tools as a graph, not a flat list.",[17,5885,5887],{"id":5886},"efficient-retrieval-prunes-context-via-typed-unification","Efficient Retrieval Prunes Context via Typed Unification",[22,5889,5890],{},"At inference, Typed DAG Retrieval operates progressively: first prune candidates using symbolic signature unification (matching input\u002Foutput types); rank survivors by semantic description similarity; filter by pre\u002Fpost-condition behavioral specs; finally disambiguate with examples on the smallest set. Only viable subgraphs materialize in context, keeping costs sublinear in library size despite growth. Theoretical results prove retrieval cost reduction, sublinear time complexity, and DAG well-formedness preservation.",[17,5892,5894],{"id":5893},"training-folds-trajectories-into-evolving-composites","Training Folds Trajectories into Evolving Composites",[22,5896,5897],{},"Successful agent trajectories distill into new validated composite tools, folding primitives into higher-level nodes. The planner fine-tunes under a DAG-induced reward that credits composites proportional to their primitive expansion size, incentivizing decomposition. Conservative updates ensure monotone co-evolution: performance never regresses as the library expands. This shaped reward yields compositional advantages, where complex solutions emerge from simpler building blocks.",[22,5899,5900],{},"CoCoDA outperforms tool-use and library-learning baselines on mathematical reasoning (GSM8K, MATH), tabular analysis, and code tasks, with an 8B model matching or exceeding a 32B teacher—showing small models scale via structured tool evolution.",{"title":90,"searchDepth":91,"depth":91,"links":5902},[5903,5904,5905],{"id":5879,"depth":91,"text":5880},{"id":5886,"depth":91,"text":5887},{"id":5893,"depth":91,"text":5894},[],{"content_references":5908,"triage":5909},[],{"relevance":125,"novelty":125,"quality":125,"actionability":126,"composite":5910,"reasoning":5911},3.8,"Category: AI & LLMs. The article discusses a novel approach to scaling tool-augmented agents using a DAG structure, which addresses specific pain points related to AI model efficiency and retrieval. It provides insights into a new method that could be applied in AI product development, though it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Ff830c7083c5c4449-cocoda-co-evolve-dags-to-scale-tool-augmented-agen-summary","2026-05-12 15:01:30",{"title":5868,"description":90},{"loc":5912},"f830c7083c5c4449","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.08399","summaries\u002Ff830c7083c5c4449-cocoda-co-evolve-dags-to-scale-tool-augmented-agen-summary",[141,140],"CoCoDA uses a compositional code DAG to jointly evolve tool libraries and planners, enabling efficient retrieval from growing libraries and letting an 8B model match or beat a 32B teacher on GSM8K and MATH benchmarks.",[],"bDy0Z5tEEQ-H-4iR5WNCkpbKzHK0FANMFtJDkI1prRU",{"id":5924,"title":5925,"ai":5926,"body":5931,"categories":5959,"created_at":99,"date_modified":99,"description":90,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":5960,"navigation":129,"path":5964,"published_at":5965,"question":99,"scraped_at":5965,"seo":5966,"sitemap":5967,"source_id":5968,"source_name":5969,"source_type":136,"source_url":5970,"stem":5971,"tags":5972,"thumbnail_url":99,"tldr":5973,"tweet":99,"unknown_tags":5974,"__hash__":5975},"summaries\u002Fsummaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary.md","Uber's OpenAI-Powered Multi-Agent AI Optimizes Earnings and Booking",{"provider":7,"model":5870,"input_tokens":5927,"output_tokens":5928,"processing_time_ms":5929,"cost_usd":5930},7179,1572,27560,0.0021993,{"type":14,"value":5932,"toc":5954},[5933,5937,5940,5944,5947,5951],[17,5934,5936],{"id":5935},"multi-agent-architecture-turns-marketplace-data-into-actionable-driver-insights","Multi-Agent Architecture Turns Marketplace Data into Actionable Driver Insights",[22,5938,5939],{},"Uber processes 40 million daily trips across 10 million drivers\u002Fcouriers in 15,000 cities over 70 countries, with 1.7 million concurrent rides influenced by traffic, weather, and events. Uber Assistant uses OpenAI frontier models to summarize complex signals like earnings trends and heatmaps into plain-language recommendations, answering queries like optimal positioning or switching rides to deliveries. This reduces cognitive load, helping new drivers ramp up faster than relying on hundreds of trips for platform learning. Experienced drivers return for follow-ups, boosting repeat engagement and on-platform time utilization. The system routes queries via multi-agent setup: nano\u002Fmini models for lightweight classification and speed, larger reasoning models for complex tasks. AI Guard screens prompts\u002Fresponses to enforce safety, privacy, policy compliance, cut hallucinations, and ensure low-latency mobile performance—critical since distrust causes quick user drop-off.",[17,5941,5943],{"id":5942},"voice-realtime-api-enables-natural-interactions-for-riders-and-drivers","Voice Realtime API Enables Natural Interactions for Riders and Drivers",[22,5945,5946],{},"OpenAI Realtime API powers voice features in the Uber app, letting users speak full intents like \"five pieces of luggage, five people, nice ride to airport\" for recommendations such as UberXL or saved locations like \"home.\" It syncs spoken and visual responses, removing multi-tap friction for complex requests. This broadens accessibility for older adults, visually impaired users, or hands-free drivers, orchestrating outcomes across the ecosystem without sequential tasks. Voice unlocks nuanced event sequences per trip\u002Fdrive, providing data to refine future features at Uber's scale.",[17,5948,5950],{"id":5949},"organizational-shifts-accelerate-ai-product-cycles","Organizational Shifts Accelerate AI Product Cycles",[22,5952,5953],{},"Uber embeds LLMs organization-wide, democratizing prompting, retrieval, evaluation, and orchestration beyond a central AI team—engaging engineering, product, legal, operations, and design for policy, testing, and UX. This fosters cross-team collaboration, faster experimentation, and iteration. Beta rollout reaches hundreds of thousands of U.S. drivers, prioritizing early-lifecycle support for better positioning\u002Ftrips, strong repeat use after value delivery, and optimized time via insights. Result: productive work, seamless transport, humanized logistics.",{"title":90,"searchDepth":91,"depth":91,"links":5955},[5956,5957,5958],{"id":5935,"depth":91,"text":5936},{"id":5942,"depth":91,"text":5943},{"id":5949,"depth":91,"text":5950},[156],{"content_references":5961,"triage":5962},[],{"relevance":124,"novelty":125,"quality":125,"actionability":126,"composite":127,"reasoning":5963},"Category: AI & LLMs. The article discusses Uber's implementation of OpenAI models in a multi-agent architecture, which directly addresses practical applications of AI in optimizing driver experiences and operational efficiency. It provides insights into how Uber leverages AI to enhance user interactions and streamline processes, making it relevant for product builders interested in AI integration.","\u002Fsummaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary","2026-05-11 15:04:26",{"title":5925,"description":90},{"loc":5964},"0ef58dbb67f9059c","OpenAI News","https:\u002F\u002Fopenai.com\u002Findex\u002Fuber","summaries\u002F0ef58dbb67f9059c-uber-s-openai-powered-multi-agent-ai-optimizes-ear-summary",[140,141],"Uber deploys OpenAI models via multi-agent architecture for Uber Assistant, delivering real-time driver guidance from marketplace data and voice-based ride booking, accelerating new driver ramp-up versus hundreds of trips via trial-and-error.",[],"yGF9AUDRqSMC2I2dNWYkDz4g_Y6jWUMkTkDUbMB341M"]