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At Kilo Code, engineers are reading or writing code themselves only about 1% of the time now, according to co-founder Emilie Schario — the rest is agents. That shift is forcing new questions onto dev teams: which systems are safe to hand over, who cleans up when models goof up, how to support multi-model architectures, and whether skyrocketing token bills mean real progress or just burned IT budget. As far as tech leads from Replit, Kilo Code, and Symbotic are concerned, it’s a natural — and welcome — evolution as agentic AI becomes embedded into more and more enterprise workflows. “Unless something's really broken or debugging, 99% of the time engineers are not reading or writing code anymore,” Emilie Schario, co-founder of Kilo Code, said at VB Transform 2026 . AI good at greenfield, not…

Presented by Rezolve Ai Most brands know something is shifting in how consumers find and choose products. What most don't know is how much of that shift has already taken place, where it's happening, or whether they're on the right side of it. That uncertainty is the problem. And the analytics stack most brands rely on isn't built to resolve it. The decision layer has moved In 2014, 82% of digital commerce started on a brand's website. By 2024 that had fallen to 38%, according to Salesforce research . The journey that used to begin at a brand's front door now begins somewhere else. Increasingly, it begins with a question asked of an AI platform and ends with an answer that shapes the purchase decision before any brand-owned touchpoint is engaged. Consumers are asking AI where to shop, what…

Chinese e-commerce and cloud giant Alibaba's famed Qwen team of AI researchers last night unveiled Qwen3.8-Max , a new flagship 2.4-trillion-parameter mixture-of-experts (MoE) multimodal large language model (LLM) that targets one of the most competitive corners of the frontier AI market: autonomous software engineering and long-horizon enterprise work. If the company's published benchmarks hold up under broader independent testing, Qwen3.8-Max doesn't merely compete with today's leading proprietary models — it surpasses several of them on some key benchmarks in agentic computing. Most notably, Qwen reports that Qwen3.8-Max scores 86.1 on the OSWorld-Verified benchmark measuring how well ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0), while also posting the highest reported score on Pa…

Enterprise teams building AI agents keep hitting the same wall: a chatbot that can answer a prompt but can't remember what the last five people asked it, and can't tell you whether last month's version actually worked. In a fireside chat with VentureBeat's Sam Witteveen at VB Transform 2026 , Asana's chief product officer, Arnab Bose, unpacked how his team tackled this problem to build a new operating system: Agentic Work Management (AWM). The product treats AI agents as coachable teammates that operate alongside humans rather than as one-to-one assistants. For product builders and developers trying to move beyond basic integrations, Bose provided a look under the hood. He detailed how Asana engineered AWM, offering a blueprint for solving real-world bottlenecks and building agentic system…

Presented by NTT DATA AIVista At VB Transform 2026 , NTT DATA AIVista CEO Bratin Saha joined VentureBeat CEO and editor-in-chief Matt Marshall to discuss the last-mile challenge of operationalizing frontier models in regulated production, where reliability, context, guardrails, and security determine whether AI delivers enterprise value. The conversation centered around the question facing every enterprise now pouring money into AI: how to convert that spending into real, tangible value. "It's not just a model, you're building a system around the model," Saha said. The last mile is the work of wrapping a frontier model in an enterprise's own data, workflows, and guardrails. In the end, regulated production turns on more than just technology, Saha said. Today, most enterprise AI projects fa…

If you have built anything with retrieval-augmented generation (RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top few that look similar to the question, and hand them to the model. For “What was our Q3 refund policy?” This works beautifully. For “What are the recurring themes across two years of customer complaints?” it falls flat — because no single chunk contains the answer. The fashionable fix is GraphRAG : Instead of feeding the model isolated snippets, you first build a knowledge graph of the entities and relationships in your corpus, then use that structure as context. The pitch is seductive. But seductive pitches deserve scrutiny, so I went through the evidence — the original Microsoft paper plus fou…

If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack. While large language models (LLMs) excel at one-off code generation, their outputs for complex data-processing tasks are typically free-form, disposable scripts. These scripts are detached from the governable workflow abstractions that MLOps teams rely on for production, making them difficult to audit or edit visually. To address this, researchers at Peking Universit…

The AI agent observability space is taking off — but how can enterprises be sure what observability products and solutions they need? Observability startup groudcover (lower case "g" intentional) announced this week that it raised $100 million in a round led by One Peak, bringing its total funding to $160 million. The company says it has more than 250 paying customers, tripled annual recurring revenue over the past year and is increasingly replacing established observability platforms inside enterprise environments. Those are company-reported figures, but together they point to growing momentum in one of enterprise software's most competitive markets. That market has long been dominated by companies including Datadog, Dynatrace, New Relic, Splunk and Grafana. Between them, they represent b…

Days after OpenAI disclosed that two frontier AI models escaped containment measures and autonomously cyberattacked the AI code sharing platform Hugging Face, OpenAI's top U.S. rival Anthropic tonight revealed that — lo and behold — it has also had models surreptitiously access the web when they weren't supposed to, and cyberattack and gain "unauthorized access" to three other organizations. Anthropic says that it ran "capture the flag" cybersecurity scenarios with three models — Claude Opus 4.7, Claude Mythos 5, and unnamed internal research prototype — with its partner, the AI security firm Irregular . Anthropic says the models were not supposed to have internet access, but that a misunderstanding with Irregular allowed them to access the internet. Once they did, they "gained unauthorize…

Just two weeks after Thinking Machines released Inkling , its first open source AI language model, the well-funded startup led by former OpenAI chief technology officer Mira Murati today introduced Inkling-Small without sacrificing much of any performance — and in fact, the new model surpasses its larger predecessor on several benchmarks. Inkling Small is a 276-billion-parameter multimodal reasoning model with a permissive Apache 2.0 license that comes within a single point of its larger sibling on the third-party Artificial Analysis Intelligence Index , despite the original Inkling being 975 billion parameters (internal model settings). It accepts text, image and audio inputs, produces text, and supports a context window of up to one million tokens. Inkling Small uses 12 billion active pa…

To quote an ancient Jedi Master "Begun, the AI price wars have!" OpenAI is sharply reducing the prices of two models in its GPT-5.6 frontier series, cutting GPT-5.6 Luna, the smallest and fastest model in the series, by 80% and GPT-5.6 Terra, the mid-tier model, by 20%, while adding a premium Fast mode for its flagship GPT-5.6 Sol model. The cuts place Luna much closer to the lowest-cost commercial models in the market and arrive just a few days after Anthropic released its highly performant Claude Opus 5 at the same price as Opus 4.8, and Google introduced Gemini 3.6 Flash and Gemini 3.5 Flash-Lite , two rival models built around lower inference costs, faster execution and more efficient agent workloads. O penAI is successfully undercutting Google's price per intelligence and attempting t…

Every time a Mastercard gets tapped, the network has less than a tenth of a second to judge how likely the purchase is to be fraudulent. It made that call across 175 billion transactions last year. Now the buyer on the other side of that judgment is starting to change, and Greg Ulrich, the company's chief AI and data officer, spelled out the consequence for the VB Transform 2026 audience in Menlo Park on July 14. "We've built a bunch of risk rules over time that were intended to stop a bot from transacting," Ulrich said. "Now we need to enable the bot to transact, so that requires a change to our risk framework and our risk rules." Ulrich joined Mastercard eleven years ago when an analytics company he worked at was acquired, and said trust struck him from day one on the job. "It's what ena…