Autonomous AI Agents Turn Devices Into Silent Workers

Autonomous AI agents are slipping out of chat windows and into phones, PCs, payment systems and dedicated gadgets, turning everyday hardware into semi‑invisible co‑workers that can browse, buy and book on our behalf — often without direct supervision. The shift promises major productivity gains, but it also creates a new class of security, liability and regulatory risks as software is trusted to act, not just advise.

Analysts expect this transition to be rapid. By the end of 2026, 40% of enterprise applications will embed task‑specific AI agents, up from less than 5% in 2025, according to a forecast from Gartner. That means tools that don’t just draft emails or summarize documents, but autonomously orchestrate workflows across finance, HR, customer service and IT.

From chatbots to “silent workers”

The new generation of agentic AI is designed to work across applications and systems, not live inside a single app. OpenAI’s Responses API and AgentKit, for example, are pitched as infrastructure for “end‑to‑end agentic workflows,” letting developers build agents that can call tools, search the web, traverse internal files and execute multi‑step tasks such as customer onboarding or expense processing with minimal human prompts, as detailed in an OpenAI product announcement and coverage by TechCrunch.

On consumer devices, dedicated hardware like the Rabbit r1 illustrates how far this idea can go. The $199 pocket gadget runs “rabbit OS,” built on a so‑called Large Action Model that learns how people use apps and then mimics those actions to book travel, shop or play music, effectively driving other services on a user’s behalf, according to the company’s launch materials and follow‑up reporting by TechCrunch. Rabbit says it has sold more than 100,000 units and is now working on an “AI‑native desktop operating system” that extends the same agentic control model to PCs, the startup disclosed in a Q1 update on its site, summarized by Rabbit.

Mainstream platforms are baking similar capabilities into operating systems. Microsoft has begun rolling out an “agentic workspace” for Windows 11 that lets AI apps like Copilot operate in their own desktop environment with access to files and other software, effectively turning the OS into an orchestrator for background agents, according to technical guidance highlighted by Windows Central. Google’s Antigravity framework, announced alongside Gemini 3, takes a comparable approach for developers, enabling autonomous coding agents that can plan and execute complex software tasks across tools and repositories, as described in documentation cited by Wikipedia.

Messaging and social platforms are moving in the same direction. Meta’s acquisition of Manus, a Chinese‑developed agentic system capable of multi‑step research, file handling and code execution, underscores its ambition to weave general‑purpose agents into products like Meta AI, WhatsApp and Facebook, according to background compiled in Wikipedia. OpenAI’s own Operator experiment, an agent that could buy groceries and file expenses for users, hinted at how quickly such helpers might jump from labs into everyday financial transactions before the company folded the concept into its broader ChatGPT agent strategy, as noted by Wikipedia.

New risks for security, law and regulators

Handing routine tasks to invisible software workers reshapes threat models. Microsoft has warned that AI agents operating inside Windows introduce “novel security risks,” including a class of attack dubbed cross‑prompt injection, where malicious text hidden in documents or web pages can hijack an agent’s instructions and trigger data exfiltration or malware installs, a risk outlined in its Windows 11 agentic OS documentation and reported by Windows Central. Research on runtime enforcement frameworks for agents, such as AgentSpec, similarly points to the need for explicit rule systems that constrain what tools an agent can invoke and under what conditions, according to an academic paper on arXiv summarized by arXiv.

Liability is also murky when agents mishandle money or data. If an autonomous helper books the wrong flight, drains a prepaid account through a bug, or leaks customer information after following a poisoned instruction on a web page, it is not yet clear whether responsibility falls on the user, the app developer, the underlying model provider or all three. Payment networks, banks and insurers have only begun to draft policies for AI‑initiated transactions, and regulators have not yet produced agent‑specific rules.

Still, the economic incentives are powerful. Gartner projects that agentic AI could account for about 30% of enterprise application software revenue by 2035, or more than $450 billion, up from roughly 2% in 2025, in its same forecast on AI agents in applications, cited by Gartner. That pull means the “silent worker” phase of AI is likely to arrive before comprehensive safeguards do — and before most people realize how many autonomous systems are already working quietly through their everyday devices.