This NASA/ESA Hubble Space Telescope image features the spiral galaxy Messier 88 (M88).
ESA/Hubble & NASA, D. Thilker
The focus of this NASA/ESA Hubble Space Telescope image released on May 29, 2026, is an active spiral galaxy on a journey lasting hundreds of millions of years. The galaxy Messier 88 (M88), also known as NGC 4501, is located about 63 million light-years away in the constellation Coma Berenices (Berenice’s Hair).
M88 is an active galaxy, which means that its center harbors a supermassive black hole that is snacking on gas and dust. Astronomers estimate the black hole is around 100 million times as massive as the Sun, and it appears to be powering outflows of gas from the galaxy’s center.
ABD Dışişleri Bakanı Marco Rubio, Demokrat Partililerin ısrarı üzerine, Kongre üyelerinin, İsrail’in nükleer prograım hakkındaki sorularını gizli bir toplantıda yanıtlamaya hazır olduğunu söyledi.
Elon Musk’ın sosyal ağı X, yeni “Videoyla Tepki Ver” özelliğini kullanıma sundu. Platformdaki yorum çeşitliliğini artırmayı amaçlayan özellik, “Yeniden Paylaş” düğmesine bir alternatif olarak sunuluyor. Özellik kapsamında kullanıcılar, paylaşımlara video ile yanıt vermeye teşvik ediliyor.
X’in ürün müdürü Nikita Bier özelliği duyururken yorumun, X’in en önemli temellerinden biri olduğuna dikkat çekti. Bier, bazen düşüncelerinizi paylaşmanın en iyi yolunun video olduğunu dile getirdi.
Bu özellik şu anda iOS kullanıcılarına açılmış durumda. X’in belirttiğine göre; özellik yakında Android ve web’e de kullanıcıların ilgisine sunulacak. Bir X’in bir sözcüsünün yaptığı açıklamaya göre; şirket bu özelliğin içerik üreticilerinin insanlarla bağlantı kurması için yeni bir yol açabileceğine inanıyor. İçerik üreticileri, bu yeni özellik sayesinde izleyicilerinden daha zengin geri bildirimler alabilecek. Aynı şekilde içerik üreticilerinin platformda gerçekleşen konuşmalara daha aktif bir şekilde katılabileceğini belirtelim.
SpaceX’in S-1 dosyasında yer alan verilere göre; Aralık 2025’teki 520 milyon kullanıcıya sahip olan X’in Mart 2026 itibarıyla 550 milyon kullanıcısı bulunuyor. X’in bir süredir platformda önemli değişikliklere imza attığını söyleyebiliriz. İçerik üreticileri için “Ücretli Ortaklık” etiketlerini kullanıma sunan şirket, gönderileri ürünlerle ilişkilendirecek reklam formatlarını test etti. Aynı şekilde İçerik Üreticisi Abonelikleri özelliği de özel başlıklar ve paylaşılabilir kartlar gibi yeni özelliklerle yenilendi. Nisan ayında ise X, tıklama tuzağı hesaplara yaptığı ödemeleri azalttı. Son olarak şirketin Communities özelliğini tamamen kapattığını da hatırlatalım. “Videoyla Tepki Ver” özelliği, şirketin platformda hayata geçirmeye çalıştığı dönüşümün son temsilcisi diyebiliriz.
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.
Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.
The sticky tape problem
The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.”
Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance.
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change.
Growing the AI vocabulary
Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a yeni framework with which to think about their own adoption of the technology.
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.”
For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.”
According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.
AI agents as connective tissue
The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”
As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a yeni business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”
The workforce, redesigned
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on yeni responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate yeni tensions that could arise in a hybrid workforce, says Shah.
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.
From output to outcome
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you’ll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a yeni set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating yeni metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise yeni questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Laying the groundwork for systems-level change
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
See how Wasmer used Codex with GPT-5.5 to build a Node.js runtime for the edge, accelerating development 10x to 20x and shipping in weeks instead of months.
If AI is the yeni power tool for developers, is there still value in artisanal craft when anyone can be a builder?
Generating code has never been easier. The bottleneck has shifted to shipping software: reviewing it, securing it, governing it, and deploying it. According to Gartner, “By 2028, asynchronous AI coding agent workflows will improve software engineering team productivity by 30% to 50%, surpassing the 0% to 20% gains from AI code assistants in 2025.” We believe realizing those gains requires agentic capabilities across every stage of the SDLC—not just code generation, but the review, security, and governance layers where work actually gets stuck. GitHub Copilot covers that full surface. Today, developers don’t just ask Copilot to write a function—they assign an agent to an issue and walk away. The agent handles the rest. The developer returns to review, steer, and approve. That’s the shift: from writing code to orchestrating outcomes. The result isn’t just faster code. It’s faster software, shipped with confidence.
That shift is playing out at enterprise scale. GitHub Copilot now serves 140,000 organizations—nearly triple the number from a year ago—with overall growth topping 100% year over year and most users leveraging multiple AI models. GitHub Copilot CLI is also seeing rapid adoption, with usage nearly doubling month over month. Together, these signals point to a platform being used with growing sophistication. As the market expands and yeni entrants emerge, we believe the depth of GitHub’s native integrations, security controls, and agentic workflows is unmatched for enterprises governing AI-assisted development at scale. Against that backdrop, we’re pleased to announce that Gartner has positioned GitHub as a Leader in the 2026 Magic Quadrant™ for Enterprise AI Coding Agents for the third consecutive year.
As part of the report, Gartner evaluated 12 vendors based on their ability to execute and completeness of vision. GitHub placed as the highest in ability to execute.
According to Gartner, “Leaders in this Magic Quadrant combine strong execution with a clear ability to shape the direction of the market. These vendors stand out for differentiated product experiences, rapid innovation and broad relevance across modern software engineering workflows, including agentic execution that extends beyond in-editor assistance into planning, testing, code review and workflow automation. They also demonstrate strong market resonance with developers and enterprises, supported by viable business models, expanding ecosystems, and enterprise-grade governance, security and operational maturity. While Leaders are not identical in approach, they consistently show that they can translate technical advances into durable market influence and remain central to how organizations adopt agentic software engineering at scale.”
We believe our continued Leader placement in the 2026 Gartner Magic Quadrant for Enterprise AI Coding Agents underscores the strength in our execution, consistently delivering innovations in agentic development, uniquely:
Honoring developer choice and flexibility by including multiple models from multiple providers and integrating Copilot into multiple surfaces, including code editors, CLIs, IDEs, and GitHub’s web, desktop, and mobile apps.
Integrating Copilot not just at the beginning, but throughout the software lifecycle, including issues, code reviews, pull requests, and actions.
Providing engineering teams with governance controls to observe, audit, and secure their use of AI.
And since the evaluation, we’ve kept building, sharpening our strengths and putting more power and capability in developers’ hands across the AI-native software lifecycle.
What’s next
We’re building on the strengths that make Copilot a leading tool for developers and enterprises, expanding its core capabilities and deepening its integrations. GitHub is uniquely positioned for the agentic era, and we’re continuing to invest across the full software lifecycle: deeper agentic workflows across every surface where developers work, expanded model choice with intelligent routing, and Copilot performance improvements grounded in understanding not just how code is generated, but how software on GitHub is actually built.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates.
Gartner, Magic Quadrant for Enterprise AI Coding Agents, Philip Walsh, Keith Holloway, Matt Brasier, Nitish Tyagi, Neha Agarwal, 20 May 2026.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from GitHub.
Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.
