Xiaomi has announced the limited closed beta release of miclaw, a new AI agent designed to control smartphones and connected smart home devices using natural language commands. The system is powered by Xiaomi’s MiMo large language model and represents the company’s latest step toward integrating AI-driven automation into its mobile ecosystem.

The invitation-only beta is currently available in China for select flagship devices, including the Xiaomi 17 Ultra Leica Edition, Xiaomi 17 Ultra, Xiaomi 17 Pro Max, Xiaomi 17 Pro, and the standard Xiaomi 17.

Unlike traditional voice assistants that respond to predefined commands, miclaw is designed to understand user intent and execute multi-step workflows across apps and system tools. The agent can access more than 50 system utilities and ecosystem services to complete tasks automatically.

Xiaomi said the system can also analyze user behavior and provide recommendations based on device data. For example, the AI agent may review subscription expenses and suggest potential savings opportunities, or adjust device settings based on contextual information such as calendar events.

AI Architecture and Multi-Step Automation

The underlying architecture uses an inference-execution loop that processes user input, selects relevant tools, and executes tasks while monitoring outcomes in real time. This process allows the AI to handle complex requests while maintaining responsiveness on the device.

A key feature of the system is its three-tier memory architecture. The AI retains important decision points and compresses interaction history to maintain context across longer workflows, supporting up to 20 consecutive steps within a single task.

Through integration with Xiaomi’s HyperConnect ecosystem and Mi Home protocols, miclaw can control a wide range of connected devices. Users can manage smart home equipment such as lighting, air conditioners, security systems, and robotic appliances using natural language instructions.

The system also supports developer integrations through the Model Context Protocol and an open software development kit. Applications can declare their capabilities to the AI agent, allowing it to dynamically discover and use third-party features.

Privacy and Edge Processing

Xiaomi said the AI system was designed with strict privacy safeguards. Most processing occurs through edge-cloud computing that keeps sensitive data on the device whenever possible.

The company also stated that personal interaction data from miclaw will not be used to train its AI models.

The development also aligns with Xiaomi’s broader technology strategy. The company has said it plans to release a new smartphone processor each year and introduce an AI assistant for international markets as it expands its chip design capabilities and global AI ecosystem.

Amazon has introduced Amazon Connect Health, a new agentic AI platform designed to automate administrative work across healthcare systems. The solution integrates with electronic health records (EHRs) to help manage patient verification, appointment scheduling, documentation, and billing tasks.

Healthcare providers often face heavy administrative workloads that reduce time available for patient care. According to Amazon Web Services, healthcare staff can spend as much as 80% of call time compiling information across multiple systems when assisting patients. Administrative complexity has also affected patient experience, with surveys showing that scheduling challenges and long wait times are a common reason patients switch providers.

Amazon Connect Health is built to address these inefficiencies by automating routine interactions while maintaining human oversight. The system can interact with patients in natural language and assist with tasks such as verifying patient identity, checking insurance eligibility, reviewing provider schedules, and booking appointments during a single call.

The platform combines Amazon Connect, AWS’s AI-powered customer experience service, with real-time connections to EHR systems. When a request requires clinical expertise or human judgment, the system can transfer the interaction to staff based on rules defined by healthcare providers.

AI Support Across the Care Journey

Amazon designed Connect Health to support clinicians before, during, and after medical visits. Before an appointment, the system reviews patient medical histories and compiles summaries that highlight relevant conditions, recent events, and long-term trends.

During visits, with patient consent, the AI can transcribe conversations between clinicians and patients and generate draft clinical notes in real time. Each element of the documentation can be traced back to the specific part of the conversation where it originated.

After the appointment, the platform generates patient-friendly summaries and prepares medical codes needed for insurance billing. The system links suggested codes to source evidence in medical records or conversation transcripts, allowing clinicians to review and finalize them quickly. According to Amazon, the process can reduce the time required to prepare billing documentation from hours or days to minutes.

Early deployments show measurable results. UC San Diego Health, which manages more than 3 million patient interactions each year, reported saving about one minute per call and redirecting roughly 630 hours per week from patient verification tasks to direct assistance. The health system also saw call abandonment rates decline by about 30%, with some departments reporting reductions of up to 60%.

Amazon One Medical has also deployed the technology across more than one million patient visits, using ambient documentation features to streamline clinical workflows.

Security and Responsible AI Design

Amazon said the system was built with healthcare-specific safeguards and privacy protections. AWS offers more than 130 HIPAA-eligible services and compliance certifications designed for healthcare organizations.

Amazon Connect Health uses evidence mapping, a feature that links AI-generated outputs to their original sources, such as conversation transcripts, medical records, or billing guidelines. The transparency allows clinicians to audit recommendations and verify information before finalizing documentation.

The AI models powering the platform were trained using healthcare-specific datasets and evaluated using multiple safety and accuracy checks, including clinician oversight and automated evaluation systems.

Amazon said the goal of Connect Health is to reduce administrative friction for providers while improving patient access to care, enabling clinicians to spend more time with patients and less time on documentation and scheduling tasks.

Google has expanded its Canvas feature within AI Mode in Search to all users in the United States using English. The feature introduces a dedicated workspace that allows users to create documents, build tools, and organize projects directly inside Google’s AI powered search interface.

Canvas functions as a dynamic side panel where users can draft content, develop interactive tools, and manage ongoing projects. The workspace integrates live information from the web and Google’s Knowledge Graph to populate projects with updated data.

The rollout marks another step in Google’s effort to transform search from a traditional query interface into a broader productivity environment powered by generative AI.

Users can access Canvas through the AI Mode tool menu and describe the project or tool they want to create. The system then generates a working prototype inside the Canvas panel, which can be edited and refined through conversational prompts.

Interactive Tools and Coding Capabilities

The updated Canvas environment includes expanded capabilities for both creative writing and coding tasks. Users can draft documents, create dashboards, or build lightweight applications without leaving the Search interface.

One example shared by Google involved an academic scholarship dashboard that aggregates application requirements, deadlines, and award amounts into a single interactive tool. Similar use cases could include study planners, travel itineraries, or data tracking dashboards.

For more advanced users, Canvas also allows access to the underlying code powering generated tools. Developers can view and modify the code directly, enabling customization or further refinement of generated applications.

Google said the feature was designed to support iterative development. After generating an initial prototype, users can test functionality and request changes through follow up prompts until the tool or document meets their needs.

AI Search as a Productivity Platform

The introduction of Canvas reflects a broader shift in how technology companies are positioning AI assisted search as a platform for creation rather than just information retrieval.

By integrating writing, coding, and project management capabilities into the search experience, Google is competing more directly with AI assistants that function as productivity tools.

The move also aligns with the industry trend toward embedding generative AI into everyday workflows. Rather than requiring separate applications for coding, note taking, or research, platforms like Canvas aim to consolidate those tasks within a single interface powered by AI models and real time web data.

Anthropic Chief Executive Dario Amodei has criticized OpenAI’s agreement with the U.S. Department of Defense, describing the company’s safety commitments as “safety theater” in a memo to employees. The comments, reported by The Information, reflect escalating tensions between leading AI developers over how their technology should be used in military contexts.

The dispute follows failed negotiations between Anthropic and the Pentagon over the military’s access to the company’s AI systems. Anthropic had previously secured a $200 million contract with the Department of Defense but declined to expand the partnership after the agency requested broader access to its technology.

According to people familiar with the talks, Anthropic asked the government to formally confirm that its models would not be used to enable mass domestic surveillance of U.S. citizens or to power fully autonomous weapons systems.

When the companies could not reach an agreement, the Pentagon instead signed a deal with OpenAI to deploy its AI models across defense infrastructure.

Debate Over Safeguards and “Lawful Use”

OpenAI said its contract with the Defense Department includes safeguards that align with similar restrictions proposed by Anthropic. The company stated that its systems would not be intentionally used for domestic surveillance of U.S. persons and that the agreement explicitly acknowledges those limitations.

However, Amodei argued in his memo that OpenAI’s messaging misrepresents the situation. He wrote that OpenAI accepted the agreement largely to avoid internal employee pushback rather than to enforce meaningful safeguards.

Amodei also criticized the contract language allowing AI systems to be used for “all lawful purposes,” a phrase Anthropic had rejected during negotiations. Critics have pointed out that legal frameworks can evolve, meaning activities considered unlawful today could become permissible in the future.

OpenAI responded in a blog post that the Defense Department has stated it does not intend to deploy AI for mass surveillance of Americans or for fully autonomous weapons systems.

Industry and Public Reaction

The dispute has become one of the most visible public disagreements among leading AI companies over military deployments of generative AI technologies.

Amodei suggested in his internal message that public sentiment may be shifting in Anthropic’s favor. Data from market intelligence firms showed a sharp increase in ChatGPT app uninstallations after OpenAI announced its Pentagon agreement, while Anthropic’s Claude application climbed in download rankings.

The controversy also unfolds as OpenAI explores further defense partnerships beyond the United States. The company is considering a deal to deploy its AI models across NATO’s unclassified networks following its Pentagon agreement, highlighting how Western defense organizations are increasingly integrating generative AI systems even as debates intensify within the industry over safeguards and governance.