Trust3 AI, a San Francisco-based startup, officially launched AgentDOS on June 15, 2026, marking a significant step forward in the governance of autonomous AI agents within the enterprise. The new control plane promises real-time observability into token consumption, a critical metric for managing both costs and security in AI-driven workflows.

AgentDOS arrives at a moment when businesses are racing to adopt AI agents—software entities that can independently perform tasks, make decisions, and interact with other systems. These agents often rely on large language models (LLMs) and consume tokens with every API call. Without oversight, token usage can spiral out of control, leading to budget overruns, security vulnerabilities, and compliance risks. Trust3 AI aims to solve that problem by giving IT administrators and security teams a unified dashboard for monitoring and governing every agent across the organization.

The Rise of Autonomous Agents and the Governance Gap

AI agents have moved from experimental labs into production environments. Microsoft’s Copilot, Salesforce’s Einstein, and custom-built agents using frameworks like AutoGen and LangChain are now handling everything from customer service to code generation. These agents are not just tools; they operate with a degree of autonomy, often accessing sensitive data and making API calls that incur costs. In enterprise settings where thousands of agents might be running concurrently, the lack of visibility into their actions creates a serious governance gap.

Trust3 AI’s CEO, in a statement accompanying the launch, noted: “Companies are deploying agents at an unprecedented scale, but most have no way to track what these agents are actually doing, how many tokens they’re burning, or whether they’re following security policies. AgentDOS provides that missing layer of control.” The platform addresses three core pain points: cost management, security governance, and operational compliance.

What AgentDOS Brings to the Table

At its heart, AgentDOS is a control plane that sits between AI agents and the enterprise IT infrastructure. It acts as a central hub for monitoring token consumption, enforcing policies, and logging all agent activity. Key features include:

  • Real-time token observability: Dashboards show token usage broken down by agent, department, project, or model. Administrators can see exactly how many tokens a specific agent consumed during a task, spot anomalies, and set alerts when limits are breached.
  • Policy-based governance: Organizations can define rules that govern agent behavior—for example, restricting an agent from accessing certain databases, limiting the number of API calls per hour, or requiring human approval for high-cost actions.
  • Cross-platform integration: AgentDOS integrates with a wide range of AI platforms and data services. The company specifically mentioned compatibility with popular LLM providers and data platforms such as Databricks, Snowflake, and others. This means enterprises can manage agents regardless of whether they run on Azure, AWS, or on-premises Windows servers.
  • Security and compliance auditing: Every interaction an agent has is logged immutably, creating an audit trail that can be used for forensic analysis or regulatory compliance. This is particularly important in industries like finance and healthcare, where agent actions must be traceable and explainable.
  • Cost allocation and chargeback: By tracking token consumption at a granular level, organizations can allocate costs back to specific teams or projects, encouraging responsible usage and preventing “shadow IT” scenarios where departments spin up unmonitored agents.

How AgentDOS Works in Practice

Deploying AgentDOS involves installing a lightweight gateway component within the enterprise network. This gateway intercepts requests from AI agents to LLM APIs and applies the configured policies. It can run as a Docker container on Windows Server or Linux, and integrates with existing identity management systems such as Azure Active Directory for role-based access control.

Once the gateway is in place, administrators gain a real-time feed of all agent activity. For instance, if a marketing agent built on GPT-4o begins generating thousands of personalized emails, the platform shows exactly how many tokens each email consumed, the total cost, and whether the agent is adhering to data privacy rules. If it attempts to access a restricted customer database, AgentDOS can block the request and alert the security team.

Trust3 AI has also emphasized the importance of agent identity. Each agent is assigned a unique identity that travels through the control plane, allowing policies to be tied to that identity regardless of where the agent runs. This approach mirrors the zero-trust security model that Microsoft has been championing within the Windows ecosystem.

Why Windows-Centric Enterprises Should Pay Attention

For organizations deeply invested in the Microsoft stack, AgentDOS offers several compelling benefits. Microsoft has been aggressively pushing AI features into Windows 11, Azure, and Microsoft 365, with Copilot agents becoming increasingly autonomous. However, the tools for managing these agents at scale are still maturing. AgentDOS fills that gap by providing a vendor-neutral control plane that works with any agent, whether it’s built with Microsoft’s Copilot Studio, OpenAI’s APIs, or open-source frameworks.

Moreover, many enterprises run AI workloads on Azure Kubernetes Service (AKS) or Windows containers. AgentDOS’s compatibility with Docker and Kubernetes makes it a natural fit for those environments. IT teams can deploy the gateway alongside their existing Windows Server infrastructure and manage agent policies through a central console that integrates with Azure Monitor or Microsoft Defender.

The ability to enforce data residency and compliance policies is another win for Windows shops. With AgentDOS, administrators can ensure that agents never send sensitive data outside of approved regions—a requirement for many government and healthcare customers. This aligns with Microsoft’s own sovereign cloud offerings and helps enterprises maintain control over their data.

Market Context and Competitive Landscape

Trust3 AI is not the only player eyeing the AI governance space. Companies like Arize, Weights & Biases, and LangSmith offer monitoring for LLM applications, but AgentDOS distinguishes itself by focusing specifically on autonomous agents and token-level observability. Traditional API management platforms like Apigee or Azure API Management can throttle calls, but they lack the agent-aware context that AgentDOS provides.

The launch comes at a time when token costs remain a major concern. As LLMs become more powerful and expensive, enterprises are seeking ways to optimize usage. AgentDOS’s ability to drill down into per-task token consumption makes it easier to identify inefficiencies—for example, an agent that uses a $10 prompt when a $0.01 prompt would suffice. Over thousands of tasks, those savings add up.

Analysts have noted that the global market for AI governance tools is expected to grow rapidly. A recent Gartner report predicted that by 2027, 60% of large enterprises will have a dedicated AI control plane to manage agentic workflows. Trust3 AI’s early entry into this market with a purpose-built solution could give it a significant advantage.

Early Impressions and Roadmap

During the launch event, Trust3 AI demonstrated AgentDOS in action using a simulated enterprise environment with over 500 active agents. The platform successfully identified an agent that was leaking tokens through an inefficient prompt loop—a common problem in agentic applications—and automatically throttled it while notifying the operations team.

Founder and CTO of Trust3 AI explained, “We built AgentDOS because we saw firsthand how easy it is for agentic systems to go off the rails. Even a well-intentioned agent can consume millions of tokens if its task loop runs amok. Our goal is to make sure that doesn’t happen without human oversight.”

The company has also outlined an ambitious roadmap. Future releases will include AI-driven anomaly detection that uses machine learning to spot unusual agent behavior patterns, deeper integration with CI/CD pipelines for policy-as-code, and a marketplace for pre-built governance policies. Trust3 AI also plans to release a version specifically optimized for edge deployments on Windows IoT, allowing factories and retail stores to manage agents on the factory floor.

The Bigger Picture: Governing the Agentic Enterprise

As AI agents become more capable, the concept of an “agentic enterprise” is emerging—one where swarms of specialized agents handle routine business processes. This vision promises huge efficiency gains but also introduces new risks. Without proper governance, an agentic enterprise can quickly become a liability. AgentDOS is one of the first tools designed from the ground up to tackle this challenge.

For Windows-focused IT teams, the message is clear: the future of work involves managing a fleet of digital workers alongside human employees. Tools that provide visibility, control, and accountability will be essential. Trust3 AI’s AgentDOS represents a significant step toward making that future safe and manageable.

Enterprises interested in piloting AgentDOS can request a demo through Trust3 AI’s website. The platform is available immediately, with pricing based on the number of agents under management and token volume monitored. A free tier is available for small-scale deployments, allowing teams to get a feel for the control plane before committing to an enterprise-wide rollout.

In an era where every token counts—both for budgets and for security—AgentDOS might just be the tool that keeps corporate AI ambitions in check.