Meta's AI agent plans reportedly include an OpenClaw competitor that can shop on Instagram Meta's AI agent plans reportedly include an OpenClaw competitor that can shop on Instagram Last week during Meta's earnings, Mark Zuckerberg said that the company is working on new AI agents for people and businesses on the company's platform. Now, we know a bit more about what those plans entail, thanks to a new report from . The publication reports that Meta is working on an OpenClaw-inspired agent currently dubbed Hatch. It sounds like the company intends for Hatch to work within its own apps, including agentic shopping on Instagram, as well as with outside services. The company has tested Hatch on simulated versions of third-party services like DoorDash, Reddit and Outlook, according to .

Anthropic announced "dreaming" for AI agents to sort through "memories" at its developer conference. Anthropic just announced a new feature called "dreaming" at the company's developer conference in San Francisco. It's part of Anthropic's recently launched AI agent infrastructure designed to help users manage and deploy tools that automate software processes. This "dreaming" aspect sorts through the transcript of what an agent recently completed and attempts to glean insights to improve the agent's performance. Folks using AI agents often send them on multistep journeys, like visiting a few websites or reading multiple files, to complete online tasks.

PCWorld explores OpenAI's new Codex AI pets, which provide visual status indicators for desktop AI agents through customizable on-screen companions. These pets address a key user experience issue by displaying red clocks when agent approval is needed and green checks upon task completion. The feature enhances multitasking efficiency by keeping users informed of AI agent activity without constant monitoring of the main interface. Whether I'm using Claude's desktop Cowork application or OpenAI's Codex coding app, I prefer that my AI agents check back with me before making high-stakes decisions. But while that makes for a safer setup, it also means my agents are often waiting around, twiddling their thumbs as they wait for me to approve their next steps. Now, if I'm sitting and watching the Cowork or Codex apps in action, I'll see right away when an agent is awaiting my approval. But if I'm working in another window or multitasking, I could easily miss the fact that an idled Cowork or Codex agent is sitting around, staring vacantly into space.

Things to Do in L.A. Tap to enable a layout that focuses on the article. FBI agents and investigators work on Sperry Avenue in Patterson, Calif., on April 7. This is read by an automated voice. Please report any issues or inconsistencies here . A Salvadoran man shot by ICE agents during an April immigration operation in Patterson has been indicted on federal assault charges.

Enterprise AI hinges on high-accuracy outputs, requiring better data context, unified architectures, and rigorous measurement frameworks, says Bavesh Patel, senior vice president at Databricks, and Rajan Padmanabhan, unit technology officer at Infosys. Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose. That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, "the quality of that AI and how effective that AI is, is really dependent on information in your ...

The 31-year-old engineer and self-described indie game developer is suspected of firing shots at the annual event attended by President Donald Trump, high-profile media figures, and US government officials. US President Donald Trump listens as acting attorney general Todd Blanche speaks during a press briefing shortly after a shooting incident at the White House Correspondents' Dinner on April 25, 2026. A 31-year-old engineer and computer scientist was identified by media reports and President Donald Trump as the suspected shooter at the White House Correspondents Dinner on Saturday night. Cole Tomas Allen, of Torrance, California, was apprehended following the firing of shots at the Washington Hilton, where Trump was scheduled to deliver remarks to a ballroom full of journalists, cabinet officials, and Hilton staff. Allen's name surfaced in media reports shortly before Trump posted two photos of a suspect following his apprehension.

However, in domains where precise and succinct expert state information is available, agents trained onsuchexpert state features usually outperform agents trained onrichobservations.

Incontrast,our work is concerned with an overall limit on the total amount of information an agent may acquire fromtheenvironment and,inturn,howthattranslates intoitsselection ofafeasible learning target.

Inthiswork,weconsider thescenario where agent limitations may entirely preclude identifying an exactly value-equivalent model, immediately giving rise to a trade-off between identifying a model that is simple enough to learn while only incurring bounded sub-optimality.

A modern data fabric makes it possible to turn existing enterprise knowledge into a trusted foundation for AI. Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey. But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data -- they must understand the business context behind it.