Microsoft has developed a new AI agent that can run autonomously around the clock to complete tasks across Microsoft 365 applications.

Microsoft Scout, unveiled at the company’s Build event Tuesday, is a new type of always-on agent based on the OpenClaw agent framework that Microsoft calls “autopilots.”

These act on a user’s behalf with their own governed Entra identity, Omar Shahine, corporate vice president at Microsoft, said in a blog post.

“Autopilots stay active in the background, understand how work gets done across your apps and systems, and take action without needing to be prompted each time,” said Shahine, a Microsoft veteran who recently announced he is leading a new team to bring OpenClaw-based personal assistants to Microsoft 365 apps.

Microsoft Scout connects to apps such as Teams, Outlook, OneDrive, and SharePoint, and accesses data from chat, email, calendar, and contacts. Accessed via Teams, it can also interact with a user’s browser and with external apps via model context protocol (MCP). The tool functions across cloud, desktop, and the web.

Shahine said Scout can reduce mundane tasks that office workers face, such as coordinating and scheduling meeting times with colleagues, or blocking times in a user’s calendar based on upcoming work commitments. “It can also spot risks, like stalled decisions, so you can address them before they become blockers,” he said.

It’s available as an “experimental release” to customers of the company’s Frontier program, Microsoft said, and will require Intune policy configuration and “opt-in attestation.”

Scout is the latest in a range of agentic tools available in Microsoft 365 apps, including Agent Mode, where users can interact with Microsoft 365 Copilot inside apps such as Word and Excel to create content, and Copilot Cowork — Microsoft’s version of Anthropic’s Claude Cowork agent that can complete tasks independently.

Despite the company’s big AI push, Microsoft has struggled to convince businesses that Microsoft 365 Copilot is worth the additional cost; it’s advertised at $30 per user each month for large businesses. Around 3% of Microsoft 365 customers pay for the add-on subscription, the company said in January, with 15 million paid users. (Microsoft announced last month that that figure has now risen to 20 million.)

It’s not clear whether Scout will be included in Microsoft 365 Copilot subscriptions or charged separately. Microsoft did not immediately provide additional details about pricing.

The launch follows Google’s recent announcement of Spark, an autonomous agent that runs within the Google Workspace application suite. Spark can also be considered a response to the launch of OpenClaw last year, initially under the name “Clawdbot.”

OpenClaw has drawn scrutiny due to apparent security flaws, but Microsoft promises Scout is built with “enterprise-grade security and controls, so it can be trusted in your organization from day one.”

For organizations that have already deployed Microsoft 365 Copilot, Scout doesn’t introduce entirely new data risks, said Jeff Pollard, vice president and principal analyst at Forrester. But it “amplifies whatever data governance problems already exist. The difference this time: instead of surfacing sensitive data to users, it can potentially act on it.

“That makes it an active risk in terms of day to day operations,” Pollard said.

Potential security concerns echo those for AI agents and are exacerbated with personal agents such as Scout: amplified data exposure (since agents can interact with data and use tools autonomously); agent manipulation or prompt injection; unexpected actions, such as using tools or acting in ways that aren’t supposed to be allowed; and observability gaps related to understanding user and agent intent and the explainability of actions.

“However, these tools exist because they make AI far more useful for individuals, so security leaders can’t draw a line in the sand and say “no.” They have to adapt and figure out how to secure them,” said Pollard.

As with most new workplace technologies, Pollard expects adoption to start with “power users” who design and develop the use cases for the agent that can then expand more widely across users.

He warned that the accuracy of tools such as Microsoft Scout can fall short of user expectations. “LLM agents still struggle with goal alignment, multi-step reasoning drifts, and tool misuse,” he said. “Users aren’t always great at explaining what they want and LLM agents aren’t always great at providing what was requested. That’s a continuing problem.”

Apple surprised everyone with the power and performance of the M1 MacBook Air when it launched the laptop in late 2020. And more than five years later, those Macs show no sign of slowing down, handling everything users care to throw at them.

The Mac still boots almost instantly, races through daily tasks, offers battery life that puts even some newer Windows laptops to shame and, perhaps most importantly, still gives millions of users no compelling reason to upgrade. 

Why the MacBook Air is still going strong

The M1 wasn’t merely better than the Intel Macs it replaced. It delivered a dramatic step forward. Silent, fast, and with remarkable energy efficiency, these laptops have proved themselves to be more reliable and longer-lasting than almost any other notebook.

Apple has continued to deliver impressive improvements ever since the M1 Macs first appeared. The recently introduced M5 MacBook Air delivers double the multi-core and 50% better single-core performance than M1; that means it provides similar performance to the MacBook Pro of around three years ago. 

Apple Silicon has improved every single year and is now extremely powerful — so much so that Apple is about to sell 10 million units of the A-series MacBook Neo, a $599 machine with an iPhone-derived chip that delivers more performance than many mainstream users need.

Meanwhile, even when using a nearly-six-year-old MacBook Air, you still experience a fast browser, responsive Office apps, great battery life and powerful photo editing capabilities. 

To the Moon and back

At the high end of Apple’s range, you’ll find Macs so accomplished they can handle almost every imaginable professional task. It means that right now, today, Apple’s product range extends from good enough to simply amazing. 

Despite heavy marketing hype from competitors who boast of their own ARM-based competitors in similar price brackets, those PCs remain compromised in comparison, if only by their use of Windows, build quality, and overall higher running costs.

Think about it: All things being equal, if you gave a typical office worker an M1 MacBook Air and an M5 MacBook Air and asked them which models they were using, how long would it take them to figure it out? 

Sure, a highly experienced Mac user would likely know. But for a lot of people, the difference would be hard to spot because what they do on their computers just isn’t particularly demanding. 

Making people happy is good for business

Surely that’s bad for Apple’s business, right? I think not. It means Apple has created a huge population of happy Mac users who are still having a good time with the Mac they acquired in 2020. Those people tell other people about their experience, which helps evangelize the platform and can’t have hurt MacBook Neo sales this year. 

They also become more interested in other Apple products, which they can afford to invest in instead of investing in the standard PC “upgrade’”cycle. After all, if you have a platform that doesn’t need an upgrade every three years, you can spend your money on something else instead. For consumers, that might be AirPods and Apple services, while for enterprise professionals that investment might become an iPad or iPhone Pro. 

Apple doesn’t mind. It still makes bank.

The company generally finds that giving people what they want is good for business. It boosts customer satisfaction scores, reduces maintenance costs, and builds repeat customers.

That long replacement cycle delivers a second benefit, too. Apple talks extensively about sustainability. With the M-series Macs, it has achieved it. 

Sustainable technology

People use these laptops longer and get more value later when they sell them on. And when they eventually get returned for recycling, Apple can tear the machines down for parts as it works toward establishing circular manufacturing within the next four years.

The M1 MacBook Air might eventually be remembered not just as the first Apple Silicon Mac, but as representing the moment when ordinary people didn’t have to worry about performance anymore. That’s why the product refuses to die — not because it’s immortal, but because for millions of users it still does everything they need. And all the M- and A-series Macs that follow it do exactly the same thing.

One more thing, however: Intel Macs will no longer be supported by macOS 27 when it ships this year. Apple typically ends support for products around 6-7 years after it removes them from sale, so when will it end support for the M1? Potentially, not too soon.

Apple only stopped selling the M1 MacBook Air in 2024, which suggests support could continue until 2030 or 2031. So, if you bought an M1 MacBook Air in 2020, you’ve actually invested in something designed to work for you for a decade. Which PCs can truly deliver that?

No wonder the M1 MacBook Air refuses to die.

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Enterprise IT leaders have always struggled with AI pricing, especially the need to pay for AI in a way that delivers ROI. But the typical IT exec may not be right person to decide how a company uses AI — and how it tries to deliver ROI — because so many line-of-business workers and partners are now experimenting with the technology on their own.

And if IT leaders don’t have a grip on how they want to use AI over the next year or two, it’s impossible to figure out how they want to pay for it. They likely hate the current method of paying per token. And other options, such as SAP’s push to charge per AI task completed, aren’t any better. 

To use a sales analogy, IT doesn’t want to pay a lot of money for leads, because there’s no way to know if those leads will generate any revenue — let alone how much. What IT leaders want is the tech equivalent of paying commission, where they only pay when a lead converts into a paying customer. And even then, they only pay a percentage of the final sale. That guarantees ROI for the enterprise.

The problem: no AI vendor would ever go for it because that approach puts too much risk on them. 

Finding a pricing model that works for both enterprise IT and AI vendors is all but impossible as long as IT is trying to deliver ROI.

Irfan Khan, president of SAP Data & Analytics, said the problem is challenging for both sides. “Everyone is scrambling to justify their investments,” and “the day one cost is not necessarily the day one value,” he said.

The problem is one of sequence. Pricing has to be negotiated and locked in long before a project starts. But with technology as new and experimental as agentic AI, there’s almost no solid information about what benefits it will (or will not) actually deliver. 

Beyond that, generative AI (genAI) and agentic AI systems might well deliver benefits that are harder to jot down in a spreadsheet. Let’s say the CFO wants to see a sharp rise in order fulfillment. But what if AI “manages to fulfill those orders more efficiently,” Khan said. “And what are the likely ripple effects of bringing more efficiencies into the process?”

Justin Greis, CEO of consulting firm Acceligence, frames the AI pricing disconnect in terms of market economics:

“The market is trying to force-fit AI into infrastructure-era pricing models, when AI is fundamentally closer to labor augmentation and business process transformation than compute consumption,” Greis said. “The core disconnect is: Enterprise IT buyers want pricing aligned to realized business value. AI vendors want pricing aligned to resource consumption and platform utilization. Those are very different economic models. 

“Token pricing is attractive to vendors because it is measurable, scalable, and predictable. But from the enterprise perspective, tokens are almost meaningless as a business metric. Nobody on the CFO side cares how many tokens were consumed if the process improvement never materialized.”

The competing pricing strategies overwhelmingly rely on just two factors: what delivers the most profit and which is the easiest to execute. Given human nature, the latter is usually the path most often taken.

It’s like one of my favorite jokes. A guy is heading to his car when he sees a man with a flashlight intently looking at the ground right next to a streetlight pole. 

“Can I help you? Are you looking for something?” the guy asks.

“Yes, I lost my car keys.”

“Silly question, but where do you last remember having them?”

“I was standing over there in that dark alley up the street. A cat screeched and I dropped my keys.”

“Wait a second — if you lost your keys over there, why are you looking here?”

“The light’s better over here.”

The lesson: taking the easy route usually beats realizing the actual objective.

Greis argued that not only would it be hard to persuade AI vendors to accept ROI pricing, but if they did  somehow agree, the unintended results could prove disastrous. 

“AI vendors cannot realistically absorb unlimited downstream business risk tied to variables they don’t control — poor internal adoption, broken processes, bad data, organizational politics, weak change management, or unclear KPIs. But the moment vendors are compensated primarily on outcomes, you create strong incentives for increasingly autonomous optimization behavior. That sounds great until organizations realize that AI systems may pursue the metric rather than the intent behind the metric,” Greis said. 

“We’ve already seen versions of this in recommendation engines, ad targeting systems, and engagement algorithms. The system learns to maximize the measurable outcome even if the methods become operationally risky, ethically questionable, reputationally damaging, or strategically misaligned. In enterprise environments, that could become dangerous very quickly. An AI system incentivized around reducing service costs might aggressively deflect legitimate customer issues. A model rewarded for sales conversion could push manipulative messaging or optimize for short-term wins at the expense of customer trust. A procurement optimization engine might lower costs while quietly increasing supplier concentration risk or degrading operational resilience.

“The more autonomous these systems become, the harder it is to separate ‘successful outcome’ from ‘acceptable behavior.’”

The best way to resolve this is potentially the most difficult. Every AI project must be approved by an AI committee whose members must ask the hard questions. What are you hoping to accomplish? If it works, specify and quantify your best-case scenario benefits. What are the most likely ways it could fail? What are the costs and disruptions most likely to happen if it fails in that way? Quantify those. 

The committee should have at least a couple of members who know exactly what these models can and cannot do to serve as a reality check. 

Next, require the LOB chief, or whoever the most senior exec involved in the project is, to share in the pain. Tie gains or losses to executive bonuses. Give those execs a reason to make sure their people are honestly and creatively thinking the project all of the way through. 

Only once that happens can a CIO know how to negotiate a fair and reasonable AI pricing deal.