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In the two years since generative AI exploded into the mainstream, we’ve moved from awe at its capabilities to a more pragmatic question: What comes next? The answer is evident in the rise of agentic AI, systems that don’t just respond to prompts but can reason, plan and pursue complex, multi-step goals autonomously.In 2026, this evolution will fundamentally reshape how engineering teams build, operate and innovate. However, any organization undergoing this transformation will require a strategic overhaul of people, processes and platforms.Agentic AI is structurally differentWhat makes agentic AI structurally different from earlier generations of developer tooling is not better prompting, but sustained execution. Frontier models can now reason across long-running, multi-step workflows, invoking tools, interpreting results and iterating over time. As this capability accelerates, entire segments of the software development lifecycle will move from human-executed to autonomously executed. By the end of this year, the defining challenge will not be whether AI can participate across engineering workflows, but how deliberately organizations design for it.The most immediate and tangible impact will be on development velocity. We are moving beyond AI as a sophisticated coding assistant to AI as an autonomous, multi-skilled team member. Agentic AI will increasingly act as a first-pass executor across the SDLC, analyzing feasibility during planning, implementing features during build, expanding test coverage during validation and surfacing risks during review; compressing weeks of coordination into continuous workflows.A recent McKinsey report highlights that AI-centric organizations are achieving 20% to 40% reductions in operating costs and 12–14 point increases in EBITDA margins, driven by automation, faster cycle times and more efficient allocation of talent and infrastructure. This isn’t just about speed; it’s about freeing human engineers from repetitive tasks to focus on the higher-order problems that require creativity and strategic thinking. The deeper gain is cognitive leverage: fewer handoffs, less context switching and reduced rediscovery of system knowledge, allowing engineers to operate at a higher level of abstraction for longer periods of time.Engineers as orchestrators, not just buildersThis shift necessitates a fundamental redefinition of engineering roles from creators to curators. It represents the core of the composable AI approach that many organizations are beginning to adopt. The engineer of 2026 will spend less time writing foundational code and more time orchestrating a dynamic portfolio of AI agents, reusable components and external services.Their value will lie in designing the overarching system architecture, defining the precise objectives and guardrails for their AI counterparts and rigorously validating the final output to ensure it is robust, secure and perfectly aligned with business goals. It’s a move from hands-on keyboard creation to high-level system design, quality assurance and strategic oversight. The core skill becomes systems thinking, not just syntax.As this shift takes hold, leading teams are converging on a simple operating model: delegate, review and own. AI agents handle first-pass execution, scaffolding, implementation, testing and documentation. Engineers review outputs for correctness, risk and alignment. Ownership of architecture, trade-offs and outcomes remains human. This clarity allows autonomy to scale without diluting accountability.Consequently, the focus of AI efforts will experience a decisive shift from prompt engineering to orchestration. Crafting the perfect prompt for a single task will become a basic, secondary skill.The primary technical challenge will be designing the sophisticated workflows and interaction protocols between multiple specialized agents. How does an agent that designs a database schema hand off its work seamlessly to an agent that writes the API and then to another that performs penetration testing? How do they collaborate, resolve conflicts and report status? This orchestration layer, which is the conductor of the AI orchestra, will become the central pillar of engineering workflows and a critical skill set for technology leaders.However, for this autonomous future to work, seamless integration with existing enterprise ecosystems is a must. An agentic AI platform that operates in a sterile, isolated lab environment is useless. It must be able to navigate, understand and operate within the complex, often messy, reality of an enterprise IT environment. This means deep integration with legacy monoliths, cloud-native CI/CD pipelines, project management tools and data lakes.Risk, governance and trust by designThis integrated technology also necessitates robust risk management and ethical considerations. How do we mitigate the risk of an autonomous agent making a flawed architectural decision that scales and impacts a production system? This will require robust guardrails, circuit breakers and comprehensive audit trails from the ground up.To counter inherent biases in training data that could lead to discriminatory outcomes, as well as the risk of agents being manipulated or jailbroken, a proactive and rigorous governance framework is essential. The 2024 Stanford AI Index Report highlights growing corporate attention to AI ethics, safety and governance as organizations struggle with rising risks. We must build trust not through black boxes, but through transparency, explainability and human-in-the-loop control points for critical decisions.Agentic AI will evolve through distinct phases. We begin with assistance, where AI supports discrete, atomic tasks; largely the stage we are in today. This progresses to augmentation, where AI manages multi-step processes and workflows within defined domains, such as autonomously overseeing a CI/CD pipeline.Ultimately, the trajectory leads to autonomy, in which AI operates across domains and makes smarter decisions guided by high-level business objectives. Each phase will demand different engineering structures, skills and governance models. Companies must be intentional and measured in their progression, avoiding the temptation to run before they can walk.This journey leads to a critical build versus buy decision that every firm will need to make. Do you invest massive resources to develop a proprietary agentic AI platform, tailored to your unique workflows and offering a potential competitive moat? Or do you leverage and customize third-party platforms from vendors, gaining speed to market but potentially sacrificing deep integration and differentiation?There is no one-size-fits-all answer, but the decision must be guided by a cold-eyed assessment of your core competencies and long-term goals, not just by the allure of the technology.Designing the hybrid human–digital workforceAll these threads culminate in the need for deliberate hybrid human-digital workforce planning. The future of engineering is not a fully automated, lights-out department; it’s a collaborative, synergistic ecosystem where human intuition and strategic oversight partner with AI speed and scale. Our focus must shift to defining the new organizational structures, communication protocols and leadership skills required to manage this blended workforce effectively.How do we evaluate the performance of an AI agent? How do we foster team cohesion between humans and digital workers? How do we retrain and upskill our existing talent? These are the profound management and cultural challenges of the near future and they require as much attention as the technology itself.Managing the transformation, not just the technologyThe promise of agentic AI is a true step-function improvement in engineering productivity, quality and innovation. But the path is fraught with technical, ethical and organizational complexity.Success will belong to those who view it not just as a new tool to implement, but as a transformative force to manage, with a clear-eyed focus on seamless integration, rigorous ethics, proactive risk management and, most importantly, the deliberate evolution of their human talent.This article is published as part of the Foundry Expert Contributor Network.Want to join?
We’re living through the single biggest tech disruption in history (and, if not the biggest, definitely the fastest). The AI revolution promises huge productivity gains by automating complex tasks, accelerating scientific breakthroughs in medicine, biotech, materials science, and democratizing access to expertise in critical industries like healthcare and education. People on the leading edge are already vibe-coding their grunt work away. It’s a great time to be alive. Or is it? The potential downsides are also clear: Prognosticating predictionistas have warned of massive job losses, brain rot, cyberattacks and worse. It’s a scary time to be alive. Since you’re reading this publication, it’s a safe assumption that you’re interested in and knowledgeable about technology, generally. And it’s become clear that technology, generally, is under dire threat from AI. How AI is harming technologyAI is killing, harming, delaying, or forcing higher prices on a wide range of technologies and tech products and services. The AI industry is: Creating catastrophic chip shortages. Major RAM makers Samsung, SK Hynix, and Micron have shifted their production to focus on High-Bandwidth Memory (HBM) needed for AI. This has led to shortages of standard DRAM and NAND chips used in smartphones, laptops, and medical devices.Driving hardware prices up. Due to the memory shortage, building non-AI electronics is becoming expensive. By early 2026, prices for standard computer memory and storage drives (SSDs) had surged because the industry’s been prioritizing high-margin AI chips over consumer parts. There’s even a trend of more people buying second-hand laptops because they can’t afford new ones. Delaying GPUs and the devices that use them. The demand for AI compute power, which usually relies on Graphics Processing Units (GPUs), has created a massive backlog for the processors and with it, the devices that use them for, you know, processing graphics. Creating COVID-like shortages. The diversion of chips to AI infrastructure is causing problems for non-AI hardware launches. Shortages of basic power and auto chips are affecting industries from automakers to home appliance makers. It’s like COVID all over again. Diverting investment in startups. Non-AI startups are struggling to raise money. Investors are funneling cash almost exclusively into AI ventures, forcing non-AI founders to pivot or adopt “AI-first” aspects (called “AI washing”), even when unnecessary. Draining brains from research labs. There’s always been a relationship between tech-related university research labs and tech. Now, this is being distorted by AI. Private AI companies are hiring away top academic researchers and engineers with massive salaries. This hollows out university departments and non-AI research labs, threatening the pipeline of future talent for critical fields like traditional software engineering. Discouraging grads from entering tech fields. As companies pivot to AI, they’re cutting entry-level jobs in other areas. US postings for entry-level roles dropped by 35% between 2023 and 2025. This disrupts the career ladder and discourages young people from pursuing non-AI tech careers.Weaponizing cyberattacks. Malicious actors are using AI to attack non-AI systems. AI enables even moderately skilled hackers to launch sophisticated attacks. Tools that clone voices and generate fake identities are breaching traditional security protocols, overwhelming standard IT infrastructure defenses. Creating a new digital divide. Technical people, developers, and those embracing AI for vibe-coding and other tasks are pulling away from less technical or less inclined people. Turning the public against the tech industry. The public’s admiration for Silicon Valley is souring in part because of the excesses of the AI sector’s toxic “996” work culture, threats to jobs, AI slop, ridiculously high salaries, skyrocketing electricity bills, and environmental damage from new data centers. There’s also the unauthorized theft of personal data and copyrighted art to train models, and the flood of deepfakes, disinformation and AI slop people see on social networks like Facebook.Destroying demand for apps. The regular software market is shifting toward “vibe-coding,” where people abandon paid app subscriptions in favor of creating their own custom, disposable applications using AI platforms like Replit, Lovable, and Cursor. Gartner predicts that consumers will cut their mobile app usage by 25% as they rely on generative AI assistants to handle tasks rather than scrolling through separate applications, even without vibe coding. Either way, the app development ecosystem is being hammered. Threatening the future of facts. AI chatbots are transforming search engines by providing direct answers instead of lists of links, a shift that starves publishers of the website traffic and revenue they need to survive. That reduces the incentives and finances for the production and publishing of new facts (for lack of a better term) while frequently presenting false information as fact. This harms technology, an industry that depends on education, new knowledge and training. All this sounds dire. And, in the short term, it’s not good. What we don’t know yet is the long-term impact of the AI revolution and whether it will prove to be a net benefit or a net harm to the non-AI technology people, business, projects, culture and communities we have loved for decades. In the meantime, it’s important to remain clear-eyed about the emerging costs to technology, even for those of us excited about AI. AI disclosure: I don’t use AI to do my writing. The words you see here are mine. I do use Gemini 3 Pro via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search to fact-check. I used a word processing application called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes.
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Casey MP Aaron Violi has been given increased responsibility in Angus Taylor’s Opposition, taking up roles in the shadow ministry. Mr Violi has been Shadow Minister for the Digital Economy, Shadow Minister for Science, Technology and Innovation, and Shadow Minister for Cyber Security. Mr Violi said it’s very exciting, having worked in the digital economy [...]
Dallas Innovates, Every Day: Here's what's new + next in North Texas.Entrepreneur and investor Mark Cuban will headline the Dallas Regional Chamber’s 2026 Convergence AI conference, presented by Accenture and the T.D. Jakes Foundation. The event returns to the Irving Convention Center at Las Colinas on March 30-31. Now in its third year, the conference is one of the largest AI business gatherings in Dallas-Fort Worth (DFW), drawing more than 750 leaders to discuss the latest innovations in AI....The post Mark Cuban to Headline Convergence AI Dallas 2026 appeared first on Dallas Innovates.
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The India AI Impact Summit highlighted indigenous innovation beyond the robot dog controversy. Universities like LPU showcased AI-powered drones and robotics, while startups like xSpecies AI developed full-stack robotics. Education-focused initiatives and grassroots STEM programs were also prominent, emphasizing practical application and hands-on learning.
AI is drowning codebases in machine-written output, but without a strategic framework, we aren’t just innovating; we’re automating the creation of legacy mess, one enterprise software leader argues. With vibe coding, a developer only needs to type a few prompts into an AI coding assistant to create a functioning app in seconds. But if you repeat that process [...]
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On February 2, wellness influencer Peter Attia stepped down from his role as chief science officer at the protein company David. On February 12, Goldman Sachs’ top lawyer Kathryn Ruemmler announced her resignation from the company. And on February 13, Hollywood agent Casey Wasserman revealed that he would sell his talent agency. All of these business execs worked in very different spheres, but their sudden departures can be traced back to the same point of origin: their names cropped up again and again in the Department of Justice’s (DOJ) latest trove of Epstein files, released in late January. Over the past few weeks, many prominent figures have stepped down from their high-profile positions amidst growing scrutiny over their relationships to the convicted sex offender Jeffrey Epstein. A new tool called “Jwiki” is dedicated to compiling all of that information in one place—on, as the name suggests, a webpage designed to mimic Wikipedia. [Screenshot: Jwiki]It’s the latest interface from a team of developers who have spent the last several months converting the notoriously dense and convoluted Epstein files into easily searchable interfaces, condensing about 3.5 million pages of material spread across .txt files, zip files, and Google Drive folders into recognizable formats. With Jwiki, instead of sifting through all of the Epstein files for individual mentions of various public figures (a nearly impossible task for members of the public), users can simply search their name and receive a succinct summary of their involvement with Epstein.How two technologists build the “Jsuite”Jwiki comes courtesy of a team led by technologists Riley Walz and Luke Igel. Walz has previously built several viral websites, including San Francisco’s “Tech Jester” and a tool to track the city’s parking cops. In November 2025, Igel, who’s the CEO of an AI company called Kino, requested Walz’s help with a tool to demystify Epstein’s emails. They built the first iteration in just one night.That initial tool, called Jmail, allows users to wade through Epstein’s seemingly endless email correspondence in a Gmail-style interface. To build it, Walz and Igel used Google’s Gemini AI to run optical character recognition (OCR) on the individual emails and map it onto a simulation of Epstein’s actual inbox. [Screenshot: Jwiki]Since then, Walz and Igel have relied heavily on vibe coding to expand the Jsuite into other apps like Jamazon; which tracks Epstein’s Amazon orders through receipts; Jflights, which converts his flight data into a searchable map; and Jphotos, which compiles the files’ thousands of photos into one massive folder. In an interview with the publication Arena on February 12, Walz and Igel said that the Jsuite is receiving an average of 10,000 visitors daily, with a peak of well over a million visitors in a single day. How to use JwikiAccording to a post from the official Jmail account on X, Walz and Igel’s team built Jwiki using their existing Jmail data. Upon first opening the site, users are greeted with a homepage that includes sections for a daily featured article, top articles by email volume, and top articles by viewership. The wiki includes entries on people, places, and events referenced in the files. [Screenshot: Jwiki]Users can either click on one of these displayed entries or look into their own areas of interest via a search bar. Clicking on Lesley Groff, Epstein’s longtime executive assistant, for example, leads to a Wikipedia-style summary that includes a breakdown of her background, correspondence with Epstein (a whopping 224,747 emails), personal connections, and visits to Epstein’s properties. It also includes a concluding section called “Criminal Exposure Assessment,” which, according to Jmail’s post on X, “cites U.S. codes that people may have been breaking as seen in the Jmail record.”[Screenshot: Jwiki]“We believe that the US government has a responsibility to fully investigate the people implicated by these files,” the X post reads. Each Jwiki entry comes with the important caveat that its contents were generated by AI, meaning it’s fairly likely the resource is peppered with some inaccuracies and potential hallucinations. To address that concern, the Jmail team announced on X on February 18 that they’d opened the site for public contributions. Users can now sign in, propose edits to articles, and view the full revision history of every change. The edits are then reviewed and either approved or denied by a team of admins. Ultimately, the team says, its goal is “Wikipedia-style open editing, where the articles self-correct.”As the Epstein files slowly begin to bring powerful business leaders to account (albeit not in a court of law), Jwiki is one of the best tools available to the public so far to understand exactly what the rich and powerful were up to behind closed doors.
Vogue Business brings you a weekly update of the most interesting stories in the world of AI that you need on your radar.Stay tuned as we spotlight AI initiatives in the fashion and beauty industry each week.
DeepSeek V4 features and upgrades: Discover the upcoming release of DeepSeek V4, a major AI advancement set to reshape the industry as competition intensifies. Find out about its features, market impact, and what it means for the future of artificial intelligence.
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Claude Code, an AI-powered coding assistant, offers a range of features designed to enhance productivity and streamline development workflows. As outlined by John Kim, effective use of Claude Code begins with a solid foundational setup, including running the assistant in your project’s root directory and creating a structured `claw.md` file. This file serves as a [...]The post 50 Claude Code Tips & Tricks for Smoother Daily Coding in 2026 appeared first on Geeky Gadgets.
Speaking at an AI Impact Summit 2026, Google CEO Sundar Pichai described the current phase as a 'transformational moment'. 'It feels like the beginning of a decade-long change driven by technology, innovation and large-scale adoption of AI,' he stated.
When venture capitalist Vinod Khosla warned ahead of the India AI Summit that IT services and BPOs could “almost completely disappear” within five
Alphabet and Google chief executive Sundar Pichai on Wednesday outlined an expansive vision for India’s artificial intelligence future, unveiling fresh investments in infrastructure, skilling, public sector partnerships and scientific research, while positioning India as central to Google’s global AI strategy.
WordPress AI Assistant will generate and edit text and images, but it will also make site changes far easier.
For a considerable time, expertise in technology was regarded as a secure path to career advancement in India, with fields such as software engineering, IT services, and programming dominating both hiring practices and professional aspirations. As the country approaches 2026, this established paradigm appears to be evolving, suggesting a significant reevaluation of the components essential [...]The post Human-Centric Careers Surpass Tech Roles in India appeared first on The CSR Journal.
Grok 4.2 is an advanced AI model designed to handle complex reasoning and decision-making tasks through a collaborative multi-agent framework. As overviewed by the AI Grid, this system integrates the expertise of four specialized agents, Captain Grok, Harper, Lucas, and Benjamin, to deliver outputs that are precise, balanced, and transparent. However, its lack of memory [...]The post Grok 4.2 Beginner Guide : Reasoning Traces & Supports Source Priority for Research appeared first on Geeky Gadgets.