Natura’s $99 Interface smart ring lets you summon AI agents with the press of a finger to complete tasks, capture thoughts, and control devices — while doubling as a health tracker.

Goodfire just launched what it says is a cheaper way to keep AI agents in check: Instead of paying a second AI to read everything an agent does, its monitors peek inside the model while it works and only call in backup when something looks fishy.

The developer of Hermes Agent raised a $90 million Series B.

Mapping structural variance against execution outputs to estimate coding agent reliability without ground truth.

The post The Consistency Quadrant: A Visual Guide to LLM Reliability appeared first on Towards Data Science.

Personal AI agents promise to shop, book flights, and make reservations for you. But deliberate blocks and anti-bot defenses are getting in the way, leaving consumers caught in the middle. A new standard aims to help.

According to the Wikimedia Foundation, rogue OpenAI agents edited wikis without permission, tried to abuse a citation tool as a proxy, and may have caused a partial Wikidata Query Service outage through massive crawling. Wikimedia says AI companies need to take responsibility for their agents instead of pushing the burden onto volunteer editors.

The article Wikimedia confirms OpenAI's rogue AI agents edited wikis, tried to compromise tools, and hammered its infrastructure appeared first on The Decoder.

The publisher of Wikipedia said Monday that OpenAI agents attempted to hack a note-taking tool it hosts, made unauthorized edits, and sent millions of resource-intensive requests to its infrastructure, in the latest instance of OpenAI systems taking harmful and potentially dangerous actions.

The objective of some of the OpenAI agents’ actions, the Wikimedia Foundation said, was to use Wikipedia as a proxy for fetching data from third-party sites. In one case, the agents posted “malicious edits” that were intended to repurpose a citation tool as a proxy. In another, the agents made unsuccessful attempts to compromise the Wikipedia Etherpad note-taking tool so it would serve the same purpose.

The agents also made millions of automated API requests, crawled millions of pages, and made hundreds of thousands of queries to the Wikidata Query Service. The last action may have contributed to a partial shutdown of the query service in May, the publisher said.

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Towards Spec-Driven Test Automation: Part 3

The post A Google Team Measured Half of My Argument, and Left the Other Half Open appeared first on Towards Data Science.

A controlled test of Gemini, DeepSeek, ChatGPT, and Claude on leakage, reporting delays, promotion effects, and structural breaks.

The post I Hid Four Traps in a Forecasting Task. Here Is What Four AI Assistants Did. appeared first on Towards Data Science.

Independent researchers discovered an agent swarm that seems to be running on Tencent's infrastructure and targeting Alibaba's map service, Amap.

From guarding one agent to steering a fleet

The post How to Govern AI Agents appeared first on Towards Data Science.

Trying to answer the question of "Can LLM agents discover?" through the lens of creativity

The post Measuring the Creativity Potential of LLM Agents appeared first on Towards Data Science.

We created a list of the most notable AI agents that can live in your text messages, from general assistants to agents designed for families, travel, and work.

I’m Matt Burns, Chief Content Officer at Insight Media Group. Each week, I round up the most important AI developments, explaining what they mean for people and organizations putting this technology to work. The thesis is simple: workers who learn to use AI will define the next era of their industries, and this newsletter is here to help you be one of them.

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OpenAI launched Dots at DevDay on Tuesday. Each Dot is an always-on agent with its own cloud computer and browser, hooked into the 4,000-plus apps that already connect to ChatGPT. In OpenAI’s own example, a Dot sees a bug alert land in Slack and starts digging in on its own. Cool.

And before Dots, Meta launched Muse, and it’s crushing the mobile install numbers previously set by ChatGPT. And before Muse, xAI shipped its version, Grok Bot, in August. 

Anthropic’s version, though different in a couple of ways, arrived two weeks ago as an update to Claude, and without a cute name or fuzzy mascot. This update folds Cowork, which has worked since July to run scheduled jobs after a user closes their laptop without being asked, into the main Claude app.

Anthropic made the right call with Cowork, whether or not it ever matches Muse’s downloads. Always-on agents are too young to have a winner, and the moats are shallow. A feature one lab ships tends to show up at its rivals within weeks or months. Meta needs Muse to be a blockbuster. Anthropic needs the people already building with Claude to hand it real, recurring work and keep coming back. Repeat use and finished jobs matter more than a flashy launch.

Always-on agents are too early to have a winner

This wave of always-on personal agents took off less than a year ago. Peter Steinberger pushed a weekend project called Clawdbot to GitHub last November. It lived on a spare computer, took prompts over WhatsApp or Telegram, and relied mostly on Claude. Anthropic sent the lawyers in January, so it became Moltbot, then OpenClaw a couple of days later. It turned into a security headache, and in February Steinberger joined OpenAI. On Tuesday, the foundation that runs the project released an early, pre-1.0 version of OpenClaw Enterprise for companies to try internally.

Ten months, three names, one OpenAI hire and an enterprise edition. Ideas move between these products faster than ever. Meta’s Nat Friedman said Muse was heavily inspired by OpenClaw, and users digging through Muse found a SOUL.md personality file nearly identical to OpenClaw’s.

Anthropic’s September merge is on the list above as a copier, two months behind OpenAI’s Work mode — though Cowork’s cloud and scheduled-task features shipped July 7, two days before Work mode. Jessica Wachtel tested both on our site across three developer tasks and found them tied on accuracy, with ChatGPT faster and Claude more thorough. When a product is this young, a me-too launch is how a lab gets its own users in the room to see what they do with it.

Meta needs Muse to be huge. The others need paying users.

Muse is a hit. Counting only iOS, Apptopia estimated 359,000 daily U.S. users in Muse’s first 12 days, compared with 231,000 for ChatGPT’s iOS app at the same point. Sensor Tower estimated more than 3.4 million downloads as of September 24, though other firms’ counts vary.

Meta needs this. Its Reality Labs division spent tens of billions of dollars on the metaverse without a mainstream product to show for it. Llama 4 landed so flat last year that Zuckerberg rebuilt his AI division around a new superintelligence lab, notably taking a 49% stake in Scale AI and hiring its founder Alexandr Wang. 

Muse is the first product from that rebuild to break through, and it got there on Meta’s user base: Apptopia found that more than 95% of Muse users also use Facebook. Muse has a free tier, paid plans at $20 and $100 a month, and connectors to Shopify and Stripe checkout, enabling agents to buy things. Meta’s route runs through scale. Scale has a downside, too. Janakiram MSV explains for TNS why Amazon started blocking Muse two weeks after launch, and it’s well worth your time because this is a new wedge in ecommerce.

OpenAI and xAI started at the paid end. The price of entry for Dots? A ChatGPT Pro plan at $100 to $500 a month, or a Business Premium or Enterprise account. Sam Altman called Dots a premium product because each one needs so much compute. Grok Bot reached 418,000 weekly users by mid-September, per Bloomberg, and xAI just launched Team Bots on Monday.

Neither company needs Meta’s install base to learn something useful. Paying customers already spend money on AI, and retention will show whether they keep using the agent once the novelty wears off. 

Anthropic shipped its version to the people already building with Claude

Anthropic did what I’d expect from labs right now. It shipped fast, shipped to paying subs, and put the agent inside the app those people already use. The merged Claude hit Pro and Max customers, with Team and Free plans coming soon. Teams have had Claude Tag in Slack since June, a proactive teammate with its own identity and audit trail. Cat Wu said an internal version accounts for about 65% of the product teams’ code changes. Developers have had Managed Agents for hosted, long-running agents since April.

What Anthropic hasn’t done is put Claude where Muse lives. Muse works inside Meta-owned WhatsApp, but Claude still asks you to open the Claude app or tag it in Slack. That workflow might matter for mainstream consumers. Teams wiring an agent into their code care more about what it can touch. Jani laid out the difference on our site in August: Grok Bots share one cloud computer and one set of logins across the whole roster, while Claude Tag joins a Slack workspace with its own service identity and channel-scoped access. Before handing agents your logins, know what you’re getting into.

Those are the users Anthropic should want. Writers on Towards Data Science have spent the year showing what builders do with always-on agents. Samir Saci put a team of OpenClaw agents on a supply chain simulation to chase down late shipments. Eivind Kjosbakken’s guide to running a fleet of OpenClaw bots walks through nightly QA bots that test an app and report bugs, plus agents that check invoices. Both ran their agents on OpenAI’s Codex. OpenAI runs its own OpenClaw agent, Androidclaw, that traces broken builds and, in some cases, merges the fix, VentureBeat reported. Anthropic needs that kind of work running on Claude.

I set up OpenClaw on an old Mac Mini this spring, and I had so many questions (and breakthroughs). That’s why builders, coders, and developers are critical to product development: They ask these questions out loud in GitHub issues and Discord threads, and the answers end up in the product.

The obvious objection is trust. It’s fair. An always-on agent holds credentials and acts while nobody is watching. xAI’s own documentation tells users not to treat Grok Bots as a security boundary, and the OpenClaw Foundation says most IT departments ban agent platforms outright. Anthropic’s defaults lean cautious: Claude asks before it acts unless you tell it otherwise, and Tag keeps its own audit trail. If builders don’t come back, or each finished task takes more supervision than doing it themselves, the experiment hasn’t worked. But if they keep finding useful work to hand off, their questions and breakthroughs become the roadmap.

That’s the race that matters for Claude, Dots and Muse: becoming the agent people trust with the next job.

The post Anthropic’s answer to Dots and Muse is already inside Claude appeared first on The New Stack.

A new approach called LEGO-Anything turns single photos into editable Blender code for 3D scenes. GPT-6 Astra leads the accompanying benchmark with up to 53 percent reconstruction accuracy. The biggest weakness across all tested agents is that they can't judge their own geometric accuracy any better than a coin flip.

The article AI agents build 3D scenes from photos but have no idea if they got it right appeared first on The Decoder.

Coordinating agent capability development with the application it powers

The post Where the Agent Development Lifecycle Fits appeared first on Towards Data Science.

Giving an LLM permission to act, in 9 steps

The post How to Build a Control Plane for AI Agents appeared first on Towards Data Science.

Flow Engineering, the agentic platform for hardware development, has announced a $50 million Series B at a $750 million valuation. The round was co-led by Antonio Gracias, founder of Valor Equity Partners and Gavin Baker, managing partner at Atreides Management. Sequoia Capital, which led Flow’s Series A, also participated in the round alongside Human Capital, […]

I traced 2,500 listing checks with Weights & Biases Weave, removed avoidable model work one change at a time, and scored every version against the same answers.

The post Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches? appeared first on Towards Data Science.

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching...

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching the same content again. A correct final answer hides those extra steps, even though they increase latency and consume tokens. Inefficiencies create more chances for failure. To improve an agent’s behavior…

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