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- Microsoft adds autonomous AI agents
News of the day
1. Microsoft's 365 Copilot now features autonomous AI agents capable of independently performing web tasks, advancing knowledge work automation. → Read more
2. Explore DevOps for AI: Learn about the challenges of deploying ML, applying DevOps principles, and designing continuous deployment pipelines for scalable AI systems. → Read more
3. Qualcomm launches AI200 and AI250 data center chips for AI inference, aiming to disrupt Nvidia and AMD with competitive TCO and advanced architecture. → Read more
4. Ilya Sutskever's deposition reveals a year-long plan to remove Sam Altman, involving Mira Murati and internal memos.→ Read more
Our take
Hi Dotikers!
This week, Microsoft takes a major step forward in augmented productivity. With Researcher with Computer Use in Microsoft 365 Copilot, AI no longer just summarizes pages ; it acts. It opens websites, clicks, fills out forms, runs code, and produces complete deliverables.
Crucially for enterprise adoption, everything runs inside an isolated Windows 365 virtual machine, with auditable traces and access controls. When this mode is enabled, organizational data is disabled by default, and the user must consent to connections, while administrators govern allowed domains and policies. Deployment begins through the Frontier program, signaling a controlled rollout.
The stakes are clear for knowledge workers: market intelligence, meeting preparation, synthesis from authenticated content, transformation into presentations or spreadsheets. Early results are promising, showing measurable progress on complex navigation benchmarks ; a 44% gain on BrowseComp and 6% on GAIA compared to the standard Researcher. More broadly, this move fits into a larger trajectory: autonomous agents have been available in Copilot Studio since March 2025, and Microsoft has added a Python interpreter along with ROI metrics to track operational impact at scale.
It’s time to move. Interface-level automation compensates for the absence of APIs and unlocks real use cases in legacy systems. But it’s important to move with guardrails: start with low-risk, closed use cases, enforce whitelists and credit limits, review logs systematically, and plan a recovery process if the agent goes off track. Let the AI click while you sip your coffee 🙂 but keep your hand near the switch. This isn’t a gimmick; it’s a scale shift that will separate teams that industrialize agentic workflows from those that merely experiment.
G.
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