Trust3 AI introduced AgentDOS on June 15, 2026, at a San Francisco event, marking a significant step toward taming the wild west of enterprise AI agent deployments. The new control plane aims to give organizations real‑time insight into what their AI agents are doing, how many tokens they are consuming, and whether those actions align with corporate security and governance policies.

AgentDOS arrives at a critical moment. Enterprises are rushing to deploy autonomous AI agents built on platforms like Microsoft Copilot Studio, LangChain, and others, but most lack the monitoring tools they take for granted with traditional software. A recent survey by Gartner found that 70% of organizations experimenting with AI agents have no real‑time visibility into agent actions or token spend. Trust3 AI wants to close that gap.

What AgentDOS Actually Does

At its core, AgentDOS is a cloud‑native observability and policy enforcement layer that sits between an organization’s AI orchestration framework and the underlying large language model (LLM) APIs. It intercepts every prompt and response, logs them immutably, and applies programmable rules before a token is ever consumed. The platform supports major LLM providers such as OpenAI, Anthropic, and Azure OpenAI Service, as well as emerging open‑source models deployed on‑premises.

Key capabilities announced at launch include:

  • Real‑time token tracking: AgentDOS provides a per‑agent, per‑session breakdown of token usage, allowing finance teams to allocate costs and IT to spot anomalies — like an agent that suddenly burns 50,000 tokens on an infinite loop.
  • Action tracing: Every API call, tool invocation, and data retrieval is recorded with full context, creating a tamper‑proof audit trail. Security teams can replay any agent session to understand exactly what happened.
  • Policy as code: Administrators define rules in a declarative language — for example, “block any agent from sending data from finance spreadsheets to external email addresses”— and AgentDOS enforces them in real time, not after the fact.
  • Multi‑platform support: Out of the box, it integrates with Microsoft Copilot Studio, AutoGen, CrewAI, and custom agent frameworks, which Trust3 AI says covers over 85% of enterprise agent builds today.
  • Cost optimization: By analyzing token usage patterns, AgentDOS recommends prompts reductions, model downgrades, or caching strategies that can cut costs by 30% or more, according to early beta testers.

Why Token Observability Matters Now

Tokens are the atomic units of modern AI. Every interaction with an LLM consumes tokens, and the cost of those tokens varies wildly — from $0.002 per 1,000 tokens for GPT‑4‑o mini to $0.12 per 1,000 tokens for Claude 3 Opus. In an enterprise setting with hundreds of agents acting autonomously, token bills can spiral out of control in days.

But cost is only part of the story. Tokens carry data. An agent that accidentally ingests a sensitive document and uses those tokens to craft an email to the wrong person represents a breach that no firewall will catch. Traditional DLP (data loss prevention) tools are blind to the contents of an LLM prompt or response. AgentDOS brings token‑level DLP by inspecting payloads in motion and applying policies before the LLM sees them.

“Token observability is the new network observability,” said Dr. Lena Park, CEO of Trust3 AI, during the launch keynote. “Ten years ago, you couldn’t imagine running a data center without monitoring every packet. Today, you can’t run AI agents without monitoring every token.”

Inside the Architecture

AgentDOS deploys as a set of lightweight sidecar proxies that sit alongside agent runtimes, plus a centralized management console. The sidecar intercepts all LLM API calls, encrypts and streams metric data to the control plane, and caches policies locally so enforcement continues even if the central console is unreachable.

The system uses a purpose‑built stream processing engine capable of analyzing token payloads at line rate — up to 100,000 tokens per second per node — without adding perceptible latency. For reference, a typical GPT‑4 call might include 2,000 tokens, so a single node can handle 50 concurrent agent sessions before needing to scale horizontally.

All telemetry is stored in a customer‑controlled data lake; nothing leaves the enterprise boundary unless explicitly forwarded to a SIEM or analytics tool. Trust3 AI emphasized that AgentDOS never sees plaintext prompt content — payloads are inspected via encrypted hashes and metadata, a crucial design choice for regulated industries.

Microsoft Copilot Studio: A First‑Class Integration

The announcement highlighted deep integration with Microsoft Copilot Studio, where enterprises are rapidly building custom copilots and autonomous agents using Microsoft’s low‑code tooling. AgentDOS plugs directly into Copilot Studio’s extensibility points via Azure API Management, meaning existing copilots can be onboarded without code changes.

“Copilot Studio makes it dangerously easy to create an agent that accesses SharePoint, Dynamics, and external APIs,” said Marcus Yee, head of AI governance at a Fortune 100 manufacturing firm that participated in the AgentDOS beta. “Without a tool like AgentDOS, we had no way to answer basic questions: Which agent used the most tokens last night? Did anyone try to exfiltrate data? Now those answers are one click away.”

Microsoft has been enhancing Copilot Studio with governance features of its own — including topic‑level analytics and the ability to set a maximum token limit per conversation — but AgentDOS goes further by providing cross‑platform oversight and programmable DLP. Trust3 AI said it is working with Microsoft to list AgentDOS on the Azure Marketplace by Q3 2026.

The Enterprise Security Angle

Security was a dominant theme in the launch presentation. Trust3 AI demonstrated a live attack scenario: a seemingly innocent HR agent was manipulated via indirect prompt injection to forward a salary spreadsheet to an external email address. Without AgentDOS, the attack succeeded silently; with AgentDOS, the system blocked the outbound API call and flagged the attempt because it violated a policy prohibiting financial data from leaving the tenant.

The demonstration resonated because prompt injection is no longer theoretical. In 2025, a major financial services firm experienced an incident where a customer‑facing chatbot was tricked into summarizing internal meeting notes, leaking merger details. AgentDOS aims to prevent exactly that kind of incident by treating every agent action as potentially hostile until verified.

Moreover, AgentDOS can apply token‑level encryption breaks: when an agent needs to process sensitive data, the sidecar can strip PII (personally identifiable information) from prompts before they reach the LLM and rehydrate the response afterward, a technique Trust3 AI calls “private inference.” This allows agents to leverage powerful public models without exposing confidential information.

Competitive Landscape

AgentDOS enters a nascent but heating market. Companies like WhyLabs offer LLM observability focused on model performance and bias, while Guardrails AI and NVIDIA NeMo Guardrails provide programmable guardrails for LLM output. Cloud vendors like AWS (with Bedrock Guardrails) and Google (with Vertex AI Model Armor) are building similar capabilities into their platforms. However, none of these solutions specifically target the token‑level, multi‑agent enterprise use case with the granularity that AgentDOS does.

Analysts note that Trust3 AI’s timing is excellent. As the EU AI Act’s high‑risk classification requirements come into force in 2026, enterprises deploying AI agents in regulated sectors will need auditable logging and real‑time intervention capabilities. AgentDOS might become a compliance necessity rather than a nice‑to‑have.

Pricing and Availability

AgentDOS is available immediately in public beta for GCP and Azure, with AWS support coming later in 2026. Pricing follows a token‑based model: $0.10 per 10,000 tokens processed, with volume discounts available. Trust3 AI also offers a free tier for up to 1 million tokens per month, intended for small teams and evaluation. Enterprise plans with dedicated tenants, advanced DLP, and private inference start at approximately $5,000 per month.

Real‑World Beta Experiences

During a closed beta that began in March 2026, a dozen enterprise customers tested AgentDOS in production environments. Feedback was largely positive, though some noted a learning curve around the policy‑as‑code syntax. One telecommunications company cut its monthly LLM bill by 42% after AgentDOS identified a recursive agent loop that was quietly burning 30 million tokens every night.

“It was like finding a money spigot that had been left running,” the company’s head of AI infrastructure shared. “We had no idea until AgentDOS highlighted it.”

On the security front, a healthcare provider used AgentDOS to enforce a policy that any prompt containing patient name and diagnosis must be processed by an on‑premises open‑source model rather than a public API. The rule prevented 1,200 potential PHI exposures in the first two weeks of deployment.

The Road Ahead

Trust3 AI’s roadmap includes deeper integration with agentic frameworks like AutoGen and Semantic Kernel, support for multi‑modal tokens (image and audio payloads), and an “agent risk score” that uses machine learning to predict which agents are most likely to violate policy based on historical behavior.

The company also plans to open‑source portions of its policy engine under a permissive license, hoping to establish AgentDOS as an industry standard for token auditing.

For enterprises deep in the AI agent journey, the message from San Francisco was clear: the era of blind trust is over. AgentDOS represents a pragmatic, immediately deployable answer to the question every CISO is asking: “What exactly are our AI agents doing right now?”

As one early adopter put it, “We finally have a dashboard that shows not just that our agents are working, but that they’re working safely and cost‑effectively. That’s the kind of transparency the board has been demanding.”