When Language Models Manipulate the Physical World
For years, interactions with large language models were confined to a chat window: users submitted a text prompt, and artificial intelligence returned sentences, code, or summaries. That boundary is now blurring. As reported by specialized outlets such as The Verge and TechCrunch, Google has confirmed the integration of a server compatible with the Model Context Protocol (MCP) into its Google Home ecosystem.
This gateway enables third-party conversational agents, whether Claude, GPT-based architectures, or autonomous agents, to interact directly with connected devices in a home or commercial building. In practice, a software agent no longer just suggests an action: it can engage door locks, adjust thermostats, check presence sensor logs, or analyze summaries from security cameras. This transition from passive generative AI to so-called "agentic" AI marks a fundamental paradigm shift in how software interacts with our daily environment.
MCP Standard Mechanics and the Hazards of Direct Action
To understand the scope of this evolution, it is helpful to clarify how the Model Context Protocol works, originally introduced by Anthropic before gaining broader industry adoption. Previously, developers had to build dedicated connectors to let an AI communicate with a database or smart home appliance. The MCP establishes a universal exchange standard: it exposes a catalogue of standardized tools to the agent, complete with strict parameters and function descriptions. When a user submits a request, the language model determines which tool to trigger and passes a structured instruction to the host infrastructure.
This tool-calling architecture gives agents remarkable efficiency, but it radically alters the cybersecurity equation. Once a probabilistic system holds write and control privileges, security can no longer rely on mere conversational guidelines or alignment filters. The UK National Cyber Security Centre (NCSC) has repeatedly highlighted the major risks posed by indirect prompt injection. In this scenario, a malicious instruction hidden within an external data stream, such as an incoming email or text captured by a camera, can hijack the agent from its original goal to execute unauthorized actions, such as disabling an alarm or exfiltrating activity logs.
The risk of "excessive agency", documented in OWASP's Top 10 for LLM Applications, arises precisely when agents receive broad permissions without human verification or deterministic compartmentalization. The robustness of an agentic system is therefore not measured by the eloquence of its underlying model, but by the strength of the architectural guardrails bounding every action.
The ProductivIA Approach: Sandboxed Orchestration via assistant_services
This shift toward agents capable of taking concrete action in their environment forms the core focus of ProductivIA. Within the Quebec-based sovereign platform, the Assistant application does not function as an isolated chatbot, but as an orchestrator of business tools. The methodological divergence, however, lies in the absolute refusal to grant unconstrained execution freedom to artificial intelligence.
To govern various applications without compromising security, ProductivIA relies on its internal assistant_services protocol. Each suite application (such as the document editor, messaging module, or contact book) formally declares a limited set of executable actions, akin to Unix pipes. When the Assistant needs to handle a cross-application task, such as cross-referencing meeting notes with a project folder to draft a message, it never accesses the underlying operating system or arbitrary system commands directly. It interacts exclusively through deterministic API contracts whose inputs, outputs, and access rights are strictly bounded by application code.
This discipline is paired with a transparency requirement enforced through the Nuage application. Every file modification, stored document, and agent-generated piece of data is logged in a structured storage environment that can be audited by the user or silo administrator. The absence of complex external dependencies and strong organizational compartmentalization prevent data exfiltration risks. Whether handling records governed by Quebec's Law 25 obligations or managing standard enterprise workflows, orchestration remains under human oversight: AI suggests and prepares actions within bounded parameters, while the infrastructure ensures the model cannot exceed its defined scope.
Looking Ahead
The rise of standardized protocols like MCP confirms that the future of digital systems hinges on collaborating software agents capable of interacting with physical infrastructure. However, this increased autonomy raises fundamental questions: what legal liability frameworks apply when a physical command is triggered by probabilistic reasoning? Will the adoption of open standards prevent proprietary lock-in in favour of architectures that communities can inspect and audit?