Rohan N. Murty

24 posts

Rohan N. Murty

Rohan N. Murty

@RohanMurty

Workfabric AI, Soroco

Beigetreten Ekim 2025
10 Folgt65 Follower
Rohan N. Murty retweetet
Claremont McKenna College
Claremont McKenna College will celebrate its 78th Commencement Ceremony and the Class of 2026 on May 16 with Akshata Murty ’02 and The Right Honourable PM Rishi Sunak MP as joint keynote speakers. cmc.edu/newsfeed/aksha…
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Ursula von der Leyen
Ursula von der Leyen@vonderleyen·
Just spoke to @MohamedBinZayed of the United Arab Emirates. I conveyed our full support and extended our condolences following Iran’s strikes and the resulting casualties. Europe strongly condemns these unjustifiable attacks. These attacks constitute a blatant violation of the UAE’s sovereignty and a clear breach of international law. And it is not the first time the UAE has faced missile and drone attacks from Iran and its proxies. Europe stands in full solidarity with partners across the region. I will continue my outreach to important regional partners.
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nasscom events
nasscom events@NasscomEvents·
Fireside Chat at #NTLF26 saw @RohanMurty, Founder, @workfabricai, in conversation hosted by Ajay Vij, Senior Country Managing Director, @Accenture, explore whether AI truly understands how work happens inside enterprises.
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Kailash Satyarthi
Kailash Satyarthi@k_satyarthi·
My heartiest gratitude to dear brother @Infosys_nmurthy ji, the legendary father of Information Technology sector in India, for launching “Seeds of Compassion”—an academic research study conducted by Ashoka University’s Centre for Social and Behaviour Change, examining the impact of our work across 1,400 child-friendly communities in India. His words were true gems of wisdom and a profound moral call: “A compassionate economy is the only sustainable economy. We all talk so much about sustainability, but we have forgotten about a compassionate economy—ensuring that the poor child is as comfortable as the rich child. We have mastered the algorithms of technology, but we are losing the rhythm of the human heart. Compassion, or Karuna, is the antidote to this modern malaise.” These reflections deeply resonate with our mission. I firmly believe that the power of compassion is the only enduring solution to the world’s most complex challenges. When compassion guides policy, innovation, and leadership, transformation becomes not only possible but inevitable. @AshokaUniv @cabcashoka
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Rohan N. Murty
Rohan N. Murty@RohanMurty·
If you and your closest competitor use the same AI models, the same platforms, and the same tools then what exactly is your edge? This is the question @imravikumars (CEO, @Cognizant ) and I set out to answer in our new @HarvardBiz article. We answer this question with data. After studying 200+ work patterns across 50+ large enterprises, the answer was clear: context. Not the models. Not the tools. Not the budget.
Workfabric AI@workfabricai

New in Harvard Business Review - from our founder @RohanMurty and @Cognizant CEO @imravikumars: Context is Your Competitive Advantage. The core insight from actual measurements in enterprises - when every company has access to the same AI models and platforms, the only remaining differentiator is how well you ground that AI in how your organization actually works. 200+ work patterns. 50+ large enterprises. One consistent finding: context, not technology, explains the performance gap. This is a follow-up to an earlier HBR article, which was the first to show real-world proof that context-driven AI produces dramatically better outcomes. Same tools, radically different results. At Workfabric AI, this is exactly what we do. We capture execution context, the decision patterns, coordination rhythms, and trade-offs that no system of record holds, and make it available to AI at the moment of decision. Context compounds. And the companies that capture it first will be the ones that pull ahead. @gnychis @guruprasad_r94 @NabeelQuryshi @juggy_17 workfabric.com/context-is-you…

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Rohan N. Murty
Rohan N. Murty@RohanMurty·
Excited to see context engineering and Workfabric AI referenced by @imravikumars, as a key part of their AI strategy, on @Cognizant’s Q4 earnings call. On the call, Cognizant leadership highlighted context engineering as a strategic priority and referenced Workfabric AI as a platform partner supporting that vision. That mention reflects something bigger than a single partnership. It signals where enterprise AI is actually headed. We are seeing a clear shift underway: * From chasing models to building systems that scale * From experimentation to production-grade AI * From isolated tools to context-aware infrastructure Context is quickly becoming core enterprise infrastructure. It is the layer that connects data, decisions, and outcomes across the organization. At Workfabric AI, we are focused on helping enterprises operationalize context so AI can work reliably in the real world, not just in demos. Proud to partner with teams that are pushing this transformation forward. A few reflections: -Organizations that engineer context alongside AI unlock materially higher productivity -Real AI impact comes from platforms paired with deep transformation expertise -The future of work will be shaped by teams that can sustain context at scale We are grateful to @imravikumars and Cognizant for their vision and partnership. The next phase of enterprise AI is being defined right now.
Workfabric AI@workfabricai

Workfabric AI and Context Engineering are now being discussed on earnings calls! On @Cognizant's (strong) Q4 earnings call, Ravi Kumar S (CEO, Cognizant) spoke about Context Engineering as a strategic focus and named WorkFabric AI as the platform partner behind it. This builds on Cognizant Technology Solutions’s earlier commitment to train and deploy 1,000 context engineers on ContextFabric, our context engineering platform, to operationalize context across the enterprise. Context is becoming infrastructure. This is what it looks like when AI moves from models → systems → real work. Proud to be partnering with @imravikumars and the Cognizant leadership team that’s building for where enterprise AI is actually going. @RohanMurty @gnychis @NabeelQuryshi @guruprasad_r94 fool.com/earnings/call-…

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Workfabric AI
Workfabric AI@workfabricai·
Most productivity problems are social, not personal. That’s why AI is shifting from single-player to multi-player. Read @RohanMurty's article for @IndiaToday, in their latest issue on themes for AI. After working with Fortune 500 teams deploying real AI agents, we've learned the biggest drain on productivity isn't individual inefficiency. It's coordination. Handoffs between teams, unclear ownership, decisions made in one place but needed in another, work stalled because context lives elsewhere. Multi-player AI isn’t “my assistant.” It’s “our colleague.” Every team builds tribal knowledge about what usually goes wrong and how similar problems were handled before. Today, that knowledge is scattered across people and systems. Multi-player AI can learn from these experiences and make them available to everyone, so answers don’t depend on who happens to remember. The biggest gains won’t come from individual copilots, but from AI that shares team context. indiatoday.in/magazine/futur…
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Workfabric AI
Workfabric AI@workfabricai·
AI agents need a harness that safeguards execution, aligns with how teams work, and evolves over time As we started building production agents for Fortune 500 companies, one thing became impossible to ignore: success has very little to do with squeezing more text into the context window. It’s tempting to frame the problem as “context engineering” in the narrow sense. Bigger prompts. Better summaries. Smarter retrieval. But once agents run for days or weeks, execute hundreds of tool calls, and move through real enterprise workflows, that framing collapses — because the environment is not static. The business changes. The team adapts. New conditions emerge. For the agent to remain useful, it must adapt alongside the team. This is not a context window problem. It is an execution and alignment problem. An agent harness is the system that sits around a model to support long-running execution. It governs how context is assembled and refreshed, how plans evolve, how tools are invoked, how state persists, and how drift or breakdowns are detected and corrected. More importantly, it enables adaptation by shaping what context the agent receives as work evolves. As agents become more autonomous, the harness increasingly holds execution together. A useful mental model: - Model = Individual reasoning capability - Context window = Short-term working memory - Harness = The system that safeguards execution, maintains alignment, and enables adaptation - Agent = The digital worker performing task-specific logic Many of the most effective “AI tools” today are best understood through this lens. Claude Code, Manus, and other vertical agents differentiate not on the model they use, but on how well their harnesses manage behavior over time: maintaining state, revisiting plans, coordinating tools, and keeping work coherent across long horizons. What became obvious in real deployments is that the harness cannot be designed from abstractions alone. Large enterprises do not run on clean workflows or fully documented logic. They run on undocumented decisions, informal processes, edge cases, exceptions, and tribal knowledge. That logic does not live in prompts or systems of record. It lives in human execution. You see it in digital interactions: - What gets rewritten before approval - Where someone pauses, escalates, or overrides - Which signals are trusted versus ignored - How exceptions are handled when reality breaks the workflow These execution-time behaviors form decision traces. They are the most reliable source of truth for how work actually happens. When signals from human work are captured, the learning signal becomes explicit. Every correction, override, escalation, or adjustment teaches the harness how work is really done. Over time, the harness evolves by observing how teams adapt. The competitive advantage is no longer the prompt. It is not even the model. It is the harness, shaped by real execution. @NabeelQuryshi @RohanMurty @gnychis @guruprasad_r94 @juggy_17
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Rohan N. Murty
Rohan N. Murty@RohanMurty·
@zarazhangrui And what about the biggest expression of luck — your birth lottery? Your genetic make up? How do you engineer that?
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ZTF Survey
ZTF Survey@ztfsurvey·
Congratulations to our @ztfsurvey architect and visionary Shri Kulkarni, who was awarded a gold medal from the @RoyalAstroSoc for 2026. This is RAS's highest honor, dating back 200 years , with former awardees including A. Einstein, Hubble, and S Hawking! ras.ac.uk/news-and-press…
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Nandish Kulkarni
Nandish Kulkarni@KulkarniNandish·
@PMOIndia @CMofKarnataka Sir. Shrinivas Kulkarni is truly a rare gem from Karnataka. It is about time our state and country also recognise his contribution to astrophysics.
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Gokul Rajaram
Gokul Rajaram@gokulr·
Startup CEOs: With very few exceptions, the first PM you should "hire" at your company should be an engineer or designer who's already on the team. I wrote the article (linked in comments) nearly a decade ago, and this is 10x more true today than it was then, due to the PDE roles converging.
Lenny Rachitsky@lennysan

Agreed

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Rohan N. Murty
Rohan N. Murty@RohanMurty·
The Royal Astronomical Society has announced its 2026 Gold Medal for my uncle, Shri Kulkarni (Astro Prof @Caltech ). This is the Society’s highest honour. Past recipients include names like Einstein, Chandra, Hubble, Babbage, Poincaré, Pickering, Hawking, Hale, among others. He is second Indian to win this medal, after Chandra. Shri has been my intellectual hero for as long as I can remember, and a big reason I pursued a PhD. I’ve always been inspired by his intensity, grit, dedication, and deep love for the work. In a family full of teachers and professors, he’s the hero in our household.
Smt. Sudha Murty@SmtSudhaMurty

Heartiest congratulations to Prof. Shrinivas Kulkarni on winning the Gold Medal at the 2026 Royal Astronomical Society Awards! As an Indian, I am proud, and as a sister, I am super proud. My congratulations to Prof. Andrew Jackson too on this honour. ras.ac.uk/news-and-press…

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Rohan N. Murty
Rohan N. Murty@RohanMurty·
The largest human-generated dataset -- context and how we work. @NabeelQuryshi @guruprasad_r94
Workfabric AI@workfabricai

How much context is there really inside the enterprise? The largest 2000 companies generate 50+ trillion digital work experiences per year. After deploying real agentic workflows inside multiple Fortune 500 companies, one fact became clear: we are underestimating how much context exists inside the enterprise, and how little of it shows up in systems of record. The tribal knowledge of how teams work lives in digital experiences created as work happens. This is the context that matters most. Here's what we've observed in deployments: -Each person generates ~2,500 digital work experiences per day -A 20-person team generates ~50,000/day -Over ~250 working days, that’s ~12.5M digital work experiences per year for one small team Scaled up, a 50,000-person company (median Fortune 500) generates ~31B digital work experiences per year. And this scale will only accelerate with more digitization of work. For comparison, social media companies are considered data giants because they collect clicks, views, graphs, and engagement at global scale. Yet enterprises generate roughly 8x more data in digital work experiences than all social media interaction data combined. This dataset was ignored for decades because there was no practical way to use it. Social media turned interaction data into trillion-dollar ad engines. Enterprises had no equivalent engine. What are these “digital work experiences”? Execution traces of how teams actually work: steps, sequence, systems touched, approvals, exceptions, dependencies, workarounds, and handoffs across email, documents, CRMs, ERPs, ticketing tools, browsers, spreadsheets, and legacy software. This is the context that matters most because it’s where judgment lives. Systems of record capture the outcome: a form, a finalized doc, a deal marked closed, an email sent. But there is often an order of magnitude more signal behind the outcome than in the outcome itself: the checks run, sources consulted, policies applied, exceptions allowed, approvals required, and the reasoning that made it correct here. Example: a workflow that looks simple in the CRM, “generate an account renewal email,” is rarely simple. The real work involves pulling usage and billing from multiple systems, understanding prior concessions, applying pricing and approval policies, coordinating with finance and legal, and relying on tribal knowledge about similar renewals. None of that execution context is captured. AI changes that. These execution traces are precisely what you need to build a company’s context backbone, a living layer that reflects how work actually gets done: what mattered, which policies applied, where exceptions were made, who approved what, and why. Context is not a feature. It is the missing substrate that allows agents to operate with real situational awareness inside a company. That’s why we are building ContextFabric. @gnychis @RohanMurty @NabeelQuryshi

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