Tag: WebDev

With agentic AI, businesses are conducting business more dynamically. Instead of traditional pre-programmed bots and static rules, agents can now “think” and invent alternate paths when unseen conditions arise. For instance, using a business domain ontology like FIBO (financial industry business ontology) can help keep agents within guardrails and avoid unwanted behavior. The bottleneck is now in the user experience (UX) layer. While agents are dynamic and transform with the data drift guided by ontology, the user interface is still very much static. These experiences with fixed fields and configurations can hamper the creative freedom given to agents. Modern standards like AG-UI (agent User interface) help streamline communication between UX and agents — but still the screens must be

As models get smarter and more capable, the "harnesses" around them must also evolve. This "harness engineering" is an extension of context engineering, says LangChain co-founder and CEO Harrison Chase in a new VentureBeat Beyond the Pilot podcast episode. Whereas traditional AI harnesses have tended to constrain models from running in loops and calling tools, harnesses specifically built for AI agents allow them to interact more independently and effectively perform long-running tasks. Chase also weighed in on OpenAI's acquisition of OpenClaw, arguing that its viral success came down to a willingness to "let it rip" in ways that no major lab would — and questioning whether the acquisition actually gets OpenAI closer to

“When you get a demo and something works 90% of the time, that’s just the first nine.” — Andrej KarpathyThe “March of Nines” frames a common production reality: You can reach the first 90% reliability with a strong demo, and each additional nine often requires comparable engineering effort. For enterprise teams, the distance between “usually works” and “operates like dependable software” determines adoption.The compounding math behind the March of Nines“Every single nine is the same amount of work.” — Andrej KarpathyAgentic workflows compound failure. A typical enterprise flow might include: intent parsing, context retrieval, planning, one or more tool calls, validation, f

San Francisco startup Anthropic continues to ship new AI products and services at a blistering pace, despite a messy ongoing dispute with the U.S. Department of War.Today, the company announced Claude Marketplace, a new offering that lets enterprises with an existing Anthropic spend commitment apply part of it toward tools and applications powered by Anthropic's Claude models but made and offered by external partners including GitLab, Harvey, Lovable, Replit, Rogo and Snowflake.According to Anthropic’s Claude Marketplace FAQ, the program is designed to simplify procurement and consolidate AI spend. Anthropic says the Marketplace is now in limited preview and that enterprises interested in using it should reach out to their Anthropic account team

Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working memory is stored.A new technique developed by researchers at MIT addresses this challenge with a fast compression method for the KV cache. The technique, called Attention Matching, manages to compact the context by up to 50x with very little loss in quality.While it is not the only memory compaction technique available, Attention Matching stands out for its execution speed and impressive information-preserving capabilities.The memory bottleneck of the KV cacheLarge language models generate their responses sequentially, one token at a time. To avoid recalculating the entire conversation history from scratch for every predicted word, the model stores a mathematical representation of every previous token it has proc

Google senior AI product manager Shubham Saboo has turned one of the thorniest problems in agent design into an open-source engineering exercise: persistent memory.This week, he published an open-source “Always On Memory Agent” on the official Google Cloud Platform Github page under a permissive MIT License, allowing for commercial usage.It was built with Google's Agent Development Kit, or ADK introduced last Spring in 2025, and Gemini 3.1 Flash-Lite, a low-cost model Google introduced on March 3, 2026 as its fastest and mos

What's old is new: the command line — the original, clunky non-graphical interface for interacting with and controlling PCs, where the user just typed in raw commands in code — has become one of the most important interfaces in agentic AI.That shift has been driven in part by the rise of coding-native tools such as Claude Code and Kilo CLI, which have helped establish a model where AI agents do not just answer questions in chat windows but execute real tasks through a shared, scriptable interface already familiar to developers — and which can still be found on virtually all PCs. For developers, the appeal is practical: the CLI is inspectable, composable and easier to control than a patchwork of custom app integrati