Meta and OpenAI launch autonomous AI agents for daily tasks

From Chatbots to Autonomous Agents

As reported by BornCity, these systems are already being deployed in various capacities. Meta is offering Muse as a free download, while OpenAI is charging a monthly fee of $100 for Dots. While the pricing models differ, both companies are pursuing the same goal of creating agents that can operate in the background of a user's digital life.

How Cloud Coordination Works

Meta's Muse is integrated with services like Facebook Marketplace and can scan bank statements to identify cancelable subscriptions. By operating in the cloud, these tools can bridge the gap between different apps and services. However, this constant background activity requires the software to have persistent permissions to access the user's accounts and data.

Real-World Success and Hurdles

The scale of adoption is already significant for some of these tools. Meta reported that Muse reached over 10 million users in the United States and Canada within the first six weeks of its launch. Of those users, approximately one-quarter are active on a daily basis, showing a high level of engagement with the autonomous features.

Privacy and Security Risks

Furthermore, interactions with these agents typically flow into the training data for the underlying models unless the user manually opts out. Security experts also warn that once an agent has seen data, revoking permissions does not delete the information it has already accessed. This persistence of data makes the initial decision to grant permissions a critical security consideration.

Expert Advice for Users

The landscape of AI usage is also being shaped by new regulatory frameworks, such as the EU AI Act. Businesses using these tools must understand the specific requirements and risk classes associated with autonomous agents. As the technology matures, the balance between seamless automation and robust data protection will remain a primary focus for developers and regulators alike.

Early Origins and Local Testing

The concept of these autonomous agents did not appear overnight. The technology has roots in earlier developments, such as a predecessor called OpenClaw that began testing with early users in San Francisco in early 2026.

These early trials helped pave the way for the current systems that now operate directly on websites and within linked accounts. This progression shows a shift from experimental local testing to broad commercial deployment by major tech corporations.

Technical Foundation and Integration

OpenAI's Dots launched in September 2026 and is built upon the GPT-6 Astra model. The system is designed to run via cloud computers, which allows it to perform tasks independently of the user's local hardware. This architecture supports integration into popular workplace platforms like Slack and Microsoft Teams.

The global reach of these tools is already evident in international markets. By October 2026, South Korea was ranked as the third-highest country in the world for weekly active users of the Dots platform.

Platform Responses and OS Security

The rise of autonomous agents has prompted reactions from other major technology players. Apple responded to reports of unauthorized reading of Mac notifications by announcing new control features for macOS in early October 2026. These updates aim to provide users with more oversight over how their devices handle sensitive information.

While Meta maintains that its integrations are based on voluntary consent, the industry is moving toward a landscape of increased scrutiny. Companies are now forced to balance the convenience of automated assistants with the need for robust security protocols to protect user privacy.

Financial and Security Best Practices

Financial experts advise against granting AI agents direct access to bank accounts because budget analyses can be performed without such a connection. This caution stems from the fact that revoking access permissions later does not delete any of the data the system has already viewed. Because these agents can store and process information during their operation, the initial permission grant is a permanent record of data exposure.

To mitigate these risks, professionals recommend treating these systems as supervised assistants rather than fully autonomous tools. Users should manually review all security-critical actions before they are finalized by the software. By maintaining a human-in-the-loop approach, people can enjoy the convenience of automation while ensuring that sensitive tasks remain under their direct oversight and control.