{"id":1162836,"date":"2026-02-26T09:06:34","date_gmt":"2026-02-26T17:06:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=1162836"},"modified":"2026-02-27T08:18:16","modified_gmt":"2026-02-27T16:18:16","slug":"corpgen-advances-ai-agents-for-real-work","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/corpgen-advances-ai-agents-for-real-work\/","title":{"rendered":"CORPGEN advances AI agents for real work"},"content":{"rendered":"\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1400\" height=\"788\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1.jpg\" alt=\"decorative icons in white on a blue and green gradient background\" class=\"wp-image-1162851\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1.jpg 1400w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-300x169.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-1024x576.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-768x432.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-1066x600.jpg 1066w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-655x368.jpg 655w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-240x135.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-640x360.jpg 640w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-960x540.jpg 960w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w\" sizes=\"auto, (max-width: 1400px) 100vw, 1400px\" \/><\/figure>\n\n\n\n<div style=\"padding-bottom:0; padding-top:0\" class=\"wp-block-msr-immersive-section alignfull row wp-block-msr-immersive-section\">\n\t\n\t<div class=\"container\">\n\t\t<div class=\"wp-block-msr-immersive-section__inner wp-block-msr-immersive-section__inner--narrow\">\n\t\t\t<div class=\"wp-block-columns mb-10 pb-1 pr-1 is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\" style=\"box-shadow:var(--wp--preset--shadow--outlined)\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<h2 class=\"wp-block-heading h3\" id=\"at-a-glance\">At a glance<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Today\u2019s AI agent benchmarks test one task at a time, while real workplace productivity requires managing dozens of interdependent tasks at once. To reflect this, we created a setting called Multi-Horizon Task Environments (MHTEs).<\/li>\n\n\n\n<li>Under multi-task loads, leading computer-using agents degrade sharply, with completion rates dropping from 16.7% to 8.7%.<\/li>\n\n\n\n<li>CORPGEN introduces <em>digital employees<\/em>, with hierarchical planning, memory isolation, and experiential learning, delivering up to 3.5 times higher completion rates than baselines across three independent agent backends.<\/li>\n\n\n\n<li>Because CORPGEN is architecture-agnostic and modular, its gains come from system design rather than any single base model, and it benefits directly as underlying models improve.<\/li>\n<\/ul>\n<\/div>\n<\/div>\t\t<\/div>\n\t<\/div>\n\n\t<\/div>\n\n\n\n<p>By mid-morning, a typical knowledge worker is already juggling a client report, a budget spreadsheet, a slide deck, and an email backlog, all interdependent and all demanding attention at once. For AI agents to be genuinely useful in that environment, they will need to operate the same way, but today\u2019s best models are evaluated one task at a time, not dozens at once.<\/p>\n\n\n\n<p>In our paper, \u201c<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/corpgen-simulating-corporate-environments-with-autonomous-digital-employees-in-multi-horizon-task-environments\/\">CORPGEN: Simulating Corporate Environments with Autonomous Digital Employees in Multi-Horizon Task Environments<\/a>,\u201d we propose an agent framework that equips AI with the memory, planning, and learning capabilities to close that gap.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"introducing-multi-horizon-task-environments\">Introducing Multi-Horizon Task Environments<\/h2>\n\n\n\n<p>Replicating the reality of workplace multitasking requires a new kind of evaluation environment. In response, we developed Multi-Horizon Task Environments (MHTEs), settings where an agent must manage multiple complex tasks simultaneously. Each task requires 10 to 30 dependent steps within a single session spanning five hours.<\/p>\n\n\n\n<p>To determine what a benchmark would need to test, we ran MHTEs at scale on some of today\u2019s leading AI agents, exposing four weaknesses. First, memory fills up. An agent cannot hold details for multiple active tasks at once. Second, information from one task interferes with reasoning about another. Third, tasks don\u2019t depend on each other in simple sequences. They form complex webs where an agent must constantly check whether upstream work is finished before it can move forward on anything downstream. Fourth, every action cycle requires reprioritizing across all active tasks, not simply resuming where the agent left off.<\/p>\n\n\n\n<p>We also tested three independent agent systems under increasing loads. As the number of concurrent tasks rose from 12 to 46, completion rates fell from 16.7% to 8.7% across all systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"corpgen-s-architecture\">CORPGEN\u2019s architecture<\/h2>\n\n\n\n<p>CORPGEN introduces <em>digital employees<\/em>: LLM-powered AI agents with persistent identities, role-specific expertise, and realistic work schedules. They operate Microsoft Office applications through GUI automation and perform consistently within MHTEs over hours of continuous activity. Figure 1 illustrates how a digital employee moves through a full workday.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1318\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-scaled.png\" alt=\"Diagram showing a digital employee's workday in three phases. Day Init on the left, where the agent loads memory and generates a daily plan. Execution Cycles in the center, where the agent repeatedly retrieves context, reasons and acts through a ReAct loop, and persists results across 50+ interleaved tasks. Day End on the right, where the agent generates a reflection and consolidates experience into long-term memory. Below the diagram, labels show the tiered memory architecture and experiential learning components.\" class=\"wp-image-1162865\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-scaled.png 2560w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-300x154.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-1024x527.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-768x395.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-1536x791.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-2048x1055.png 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/day_in_life_color_AH-240x124.png 240w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><figcaption class=\"wp-element-caption\">Figure 1. Each day begins with a structured plan and memory loaded from previous sessions. The agent then works through overlapping tasks in repeated cycles, storing key outcomes at day\u2019s end to inform the next session.<\/figcaption><\/figure>\n\n\n\n<p>CORPGEN addresses each of the four weaknesses of concurrent task execution\u2014memory overload, cross-task interference, dependency complexity, and reprioritization\u2014in a targeted way. Hierarchical planning breaks objectives into daily goals and then into moment-to-moment decisions, allowing the agent to act from a structured plan instead of reviewing all available tasks before each step.<\/p>\n\n\n\n<p>Subagents perform complex operations like web research in isolated contexts, preventing cross-task contamination. A tiered memory system enables selective recall of task-related information rather than retaining everything in active context. Adaptive summarization compresses routine observations while preserving critical information, keeping memory growth controlled. <\/p>\n\n\n\n<p>Because these mechanisms are not tied to a specific base model, we tested CORPGEN across three different agents. In each case, we observed consistent gains. The improvements came from the architecture, not from the strength of any particular model. Figure 2 shows how they fit together within CORPGEN&#8217;s architecture.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1318\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-scaled.png\" alt=\"Architecture diagram of the CORPGEN framework. At center is the Digital Employee with persistent identity, execution engine, cognitive tools, sub-agents, and context management. On the left, Hierarchical Planning decomposes strategic objectives into tactical plans and operational actions. On the right, Sub-Agents as Tools shows a Research Agent and Computer-Use agent (UFO2) operating in isolated contexts. At the bottom, the Tiered Memory Architecture spans working memory, structured long-term memory, and semantic memory via Mem0. Experiential Learning in the bottom right captures successful trajectories and routes feedback to UFO2. Multi-Employee Collaboration at the top shows async communication via Email and Teams with no shared state.\" class=\"wp-image-1162863\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-scaled.png 2560w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-300x154.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-1024x527.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-768x395.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-1536x791.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-2048x1055.png 2048w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/corpgen_arch_color_AH2-240x124.png 240w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><figcaption class=\"wp-element-caption\">Figure 2. Four mechanisms support concurrent task execution in CORPGEN: hierarchical planning, isolated subagents, tiered memory, and adaptive summarization.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-digital-employees-collaborate\">How digital employees collaborate<\/h2>\n\n\n\n<p>When multiple digital employees operate in the same environment, collaboration takes shape through standard communication channels, without predefined coordination rules. One employee sends an email requesting data; another picks it up in the next cycle, uses its memory to process it, and responds. This exchange mirrors real workplace communication.<\/p>\n\n\n\n<p>There is no shared internal state between agents. Coordination occurs entirely through email and Microsoft Teams, the same channels many workers use. Over time, these independent exchanges form recognizable organizational patterns. Some agents take on leadership roles; others provide support; shared documents become the connective tissue.<\/p>\n\n\n\n<p>When a communication path breaks, such as an email delivery error, agents reroute messages through alternate channels to keep work moving. The result is a virtual organization that behaves like a real one without being explicitly programmed to do so.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"evaluating-corpgen\">Evaluating CORPGEN<\/h2>\n\n\n\n<p>We evaluated CORPGEN on a multi-task benchmark that combined up to 46 tasks into a single six-hour session. Three findings stood out.<\/p>\n\n\n\n<p><strong>Baselines degrade as load increases; CORPGEN does not.<\/strong> All three baseline agent systems showed steady performance declines as task load rose. CORPGEN, by contrast, maintained or improved its completion rates at higher loads. At 46 tasks, CORPGEN completed 15.2% of tasks, compared with 4.3% for the baselines, roughly 3.5 times more.<\/p>\n\n\n\n<p><strong>Experiential learning drives the largest gains.<\/strong> We introduced CORPGEN\u2019s components sequentially: first the orchestration layer, then cognitive tools, and finally experiential learning. The first two produced moderate improvements. Experiential learning, in which agents store records of completed tasks and reuse them when they encounter structurally similar work, produced the largest increase, raising completion rates from 8.7% to 15.2%.<\/p>\n\n\n\n<p><strong>Evaluation methodology changes the picture.<\/strong> When we inspected the actual output files produced by agents, the results agreed with human judgements roughly 90% of the time. Evaluation based on screenshots and action logs agreed only about 40% of the time. This gap suggests that common evaluation approaches may underestimate what agents actually accomplish in practice.<\/p>\n\n\n\n\t<div class=\"border-bottom border-top border-gray-300 mt-5 mb-5 msr-promo text-center text-md-left alignwide\" data-bi-aN=\"promo\" data-bi-id=\"999693\">\n\t\t\n\n\t\t<p class=\"msr-promo__label text-gray-800 text-center text-uppercase\">\n\t\t<span class=\"px-4 bg-white display-inline-block font-weight-semibold small\">Spotlight: Event Series<\/span>\n\t<\/p>\n\t\n\t<div class=\"row pt-3 pb-4 align-items-center\">\n\t\t\t\t\t\t<div class=\"msr-promo__media col-12 col-md-5\">\n\t\t\t\t<a class=\"bg-gray-300 display-block\" href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-forum\/?OCID=msr_researchforum_MCR_Blog_Promo\" aria-label=\"Microsoft Research Forum\" data-bi-cN=\"Microsoft Research Forum\" target=\"_blank\">\n\t\t\t\t\t<img decoding=\"async\" class=\"w-100 display-block\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2025\/05\/Research-Forum-hero_1400x788.jpg\" alt=\"Research Forum | abstract background with colorful hexagons\" \/>\n\t\t\t\t<\/a>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"msr-promo__content p-3 px-5 col-12 col-md\">\n\n\t\t\t\t\t\t\t\t\t<h2 class=\"h4\">Microsoft Research Forum<\/h2>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<p id=\"microsoft-research-forum\" class=\"large\">Join us for a continuous exchange of ideas about research in the era of general AI. Watch the first four episodes on demand.<\/p>\n\t\t\t\t\n\t\t\t\t\t\t\t\t<div class=\"wp-block-buttons justify-content-center justify-content-md-start\">\n\t\t\t\t\t<div class=\"wp-block-button\">\n\t\t\t\t\t\t<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-forum\/?OCID=msr_researchforum_MCR_Blog_Promo\" aria-describedby=\"microsoft-research-forum\" class=\"btn btn-brand glyph-append glyph-append-chevron-right\" data-bi-cN=\"Microsoft Research Forum\" target=\"_blank\">\n\t\t\t\t\t\t\tWatch on-demand\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div><!--\/.msr-promo__content-->\n\t<\/div><!--\/.msr-promo__inner-wrap-->\n\t<\/div><!--\/.msr-promo-->\n\t\n\n\n<h2 class=\"wp-block-heading\" id=\"implications-and-looking-forward\">Implications and looking forward<\/h2>\n\n\n\n<p>The results suggest that memory and retrieval, not just raw model capability, may be a key bottleneck in getting agents to work in the real world. The largest gains came from experiential learning. Agents that learn from prior successes and apply those patterns to structurally similar tasks build an advantage over systems that respond to each task in isolation.<\/p>\n\n\n\n<p>CORPGEN also opens a new lens on how AI agents collaborate. Next steps include testing whether agents can maintain memory across multiple workdays and how they coordinate when working in teams. We are also exploring ways to make agents faster and more reliable by combining different methods of interacting with software.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"acknowledgments\">Acknowledgments<\/h2>\n\n\n\n<p>This work is a result of a collaboration between the Office of the CTO at Microsoft and the Microsoft AI Development Accelerator Program (MAIDAP). We would like to thank the Microsoft Security Research team for providing resources that supported this research. We also thank the members of the Microsoft <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/microsoft\/UFO\" target=\"_blank\" rel=\"noopener noreferrer\">UFO2<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> team and the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/github.com\/mem0ai\/mem0\" target=\"_blank\" rel=\"noopener noreferrer\">Mem0<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> project for their open-source contributions, which enabled key components of the CORPGEN architecture, and the OSWorld team for the benchmark that served as the foundation for our multi-task evaluation.<\/p>\n\n\n\n<p>Finally, we thank the many contributors to this research: Charlotte Siska, Manuel Ra\u00fal Mel\u00e9ndez Luj\u00e1n, Anthony Twum-Barimah, and Mauricio Velazco.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>By mid-morning, a typical knowledge worker is already juggling a client report, a budget spreadsheet, a slide deck, and an email backlog, all interdependent and all demanding attention at once. For AI agents to be genuinely useful in that environment, they will need to operate the same way, but today\u2019s best models are evaluated one [&hellip;]<\/p>\n","protected":false},"author":43518,"featured_media":1162851,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Abubakarr Jaye","user_id":"44097"},{"type":"user_nicename","value":"Nigel Boachie Kumankumah","user_id":"44106"},{"type":"user_nicename","value":"Chidera Biringa","user_id":"44102"},{"type":"user_nicename","value":"Anjel Patel","user_id":"44109"},{"type":"user_nicename","value":"Dayquan Julienne","user_id":"44110"},{"type":"user_nicename","value":"Tianwei Chen","user_id":"44111"},{"type":"user_nicename","value":"Sulaiman Vesal","user_id":"43888"}],"msr_hide_image_in_river":0,"footnotes":""},"categories":[1],"tags":[],"research-area":[13556],"msr-region":[],"msr-event-type":[],"msr-locale":[268875],"msr-post-option":[243984],"msr-impact-theme":[],"msr-promo-type":[],"msr-podcast-series":[],"class_list":["post-1162836","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-blog","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-post-option-blog-homepage-featured"],"msr_event_details":{"start":"","end":"","location":""},"podcast_url":"","podcast_episode":"","msr_research_lab":[],"msr_impact_theme":[],"related-publications":[],"related-downloads":[],"related-videos":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-events":[],"related-researchers":[{"type":"user_nicename","value":"Abubakarr Jaye","user_id":44097,"display_name":"Abubakarr Jaye","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/ajaye\/\" aria-label=\"Visit the profile page for Abubakarr Jaye\">Abubakarr Jaye<\/a>","is_active":false,"last_first":"Jaye, Abubakarr","people_section":0,"alias":"ajaye"},{"type":"user_nicename","value":"Nigel Boachie Kumankumah","user_id":44106,"display_name":"Nigel Boachie Kumankumah","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/nkumankumah\/\" aria-label=\"Visit the profile page for Nigel Boachie Kumankumah\">Nigel Boachie Kumankumah<\/a>","is_active":false,"last_first":"Kumankumah, Nigel Boachie","people_section":0,"alias":"nkumankumah"},{"type":"user_nicename","value":"Chidera Biringa","user_id":44102,"display_name":"Chidera Biringa","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/cbiringa\/\" aria-label=\"Visit the profile page for Chidera Biringa\">Chidera Biringa<\/a>","is_active":false,"last_first":"Biringa, Chidera","people_section":0,"alias":"cbiringa"},{"type":"user_nicename","value":"Anjel Patel","user_id":44109,"display_name":"Anjel Patel","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/anjelpatel\/\" aria-label=\"Visit the profile page for Anjel Patel\">Anjel Patel<\/a>","is_active":false,"last_first":"Patel, Anjel","people_section":0,"alias":"anjelpatel"},{"type":"user_nicename","value":"Dayquan Julienne","user_id":44110,"display_name":"Dayquan Julienne","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/djulienne\/\" aria-label=\"Visit the profile page for Dayquan Julienne\">Dayquan Julienne<\/a>","is_active":false,"last_first":"Julienne, Dayquan","people_section":0,"alias":"djulienne"},{"type":"user_nicename","value":"Tianwei Chen","user_id":44111,"display_name":"Tianwei Chen","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tianweichen\/\" aria-label=\"Visit the profile page for Tianwei Chen\">Tianwei Chen<\/a>","is_active":false,"last_first":"Chen, Tianwei","people_section":0,"alias":"tianweichen"},{"type":"user_nicename","value":"Sulaiman Vesal","user_id":43888,"display_name":"Sulaiman Vesal","author_link":"<a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/svesal\/\" aria-label=\"Visit the profile page for Sulaiman Vesal\">Sulaiman Vesal<\/a>","is_active":false,"last_first":"Vesal, Sulaiman","people_section":0,"alias":"svesal"}],"msr_type":"Post","featured_image_thumbnail":"<img width=\"960\" height=\"540\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-960x540.jpg\" class=\"img-object-cover\" alt=\"decorative icons in white on a blue and green gradient background\" decoding=\"async\" loading=\"lazy\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-960x540.jpg 960w, 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https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1-1280x720.jpg 1280w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2026\/02\/CORPGEN-BlogHeroFeature-1400x788-1.jpg 1400w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/>","byline":"","formattedDate":"February 26, 2026","formattedExcerpt":"By mid-morning, a typical knowledge worker is already juggling a client report, a budget spreadsheet, a slide deck, and an email backlog, all interdependent and all demanding attention at once. 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