Live at: agentos.services

Know the moment. Operate with context. agentos.services

Console is still in active development, but you’re free to break it. Super AgentOS ⚡

Introducing 𝗦𝗲𝗲𝗱𝗮𝗻𝗰𝗲 𝗠𝗼𝘃𝗶𝗲 𝗠𝗼𝗱𝗲𝗹𝘀; quality, fast, and efficient Seedance routes for governed movie production.

Explore: agentos.services/appstore

Every collection is quality-gated and workspace-first. Apps open through AgentOS Web Runtime; verified device packages appear only when they exist. Studio is hardened across Chrome, Firefox and iPhone WebKit.

Introducing COLLECTIONS on AgentOS Marketplace. Work & Collaboration. Finance & Payments. Crypto Trading. Solana. DeFi. Research. Build & Deploy. Security. Find the stack for the outcome, not another wall of listings.

INTRODUCING AgentOS WEB RUNTIME. Web Apps on AgentOS no longer feel like links opening in a browser. Install once to your workspace, then open the real app inside AgentOS; users can download to their local devices if native mobile or desktop is supported, no visible URL, no fake native packaging, across desktop and mobile. One operating layer. Every app.

The comparison gets more interesting when you look at what antivirus actually does. Microsoft Defender provides continuous monitoring, behavior-based detection and real-time intervention rather than relying only on a scan after something has already happened. That same architectural idea becomes critical for autonomous agents. An agent can reason correctly and still take the wrong action. It can call the wrong tool. Use the wrong parameters. Produce an unreliable result. Violate a policy. Or behave differently when its environment changes. So evaluation cannot exist only as a benchmark you run before deployment. AgentOS turns evaluation into infrastructure: an always-present layer for observing and evaluating agent behavior during operation. Not simply: “Is this model intelligent?” But: “Can this agent be trusted to perform this action, in this context, right now?” That shift from static testing toward runtime controls and evaluation is becoming a major requirement for production agent systems.

This is where multi-intelligence matters. Super AgentOS compares external intelligence, challenges conflicting conclusions and produces a structured, evidence-backed assessment instead of relying on one provider’s answer. Research only; not financial advice. agentos.services

What happens to markets when conflict involving Iran escalates? We asked Super AgentOS to conduct comprehensive financial research covering energy, shipping, equities, currencies, inflation and geopolitical risk.

AgentOS is now live in Public Beta. BUILT IN PUBLIC. One command. One governed workspace. Real execution you can verify. Super AgentOS turns intent into completed work across intelligence, tools and capabilities. Here’s what that means in practice;

We're just few days into the second 30-days of our Public Build Marathon.

Our official GitHub is now private as we build the next chapter of AgentOS. github.com/AgentOS-Technolog…

AgentOS by PRIME has moved to AgentOS Technologies Ltd. New home. Bigger mission. Same commitment to intelligent systems.

Think about how Microsoft Windows changed personal computing. Windows doesn’t simply let every file, process, download or network connection do whatever it wants. Microsoft Defender sits in the background continuously evaluating activity, looking for threats and intervening when something appears unsafe. AgentOS applies a similar philosophy to AI agents but the thing being evaluated is intelligence in motion. The AgentOS Evaluation Layer is designed to sit between autonomous agents and the environments they act upon, evaluating what agents are doing as they operate. Windows needed an antivirus layer. The Agent Economy needed an Evaluation Layer.

The model wars are finally coming to an end. Bigger benchmarks, flashier demos, endless leaderboard debates; we’ve seen it all. Now comes the part that matters: AI that ships, automates, solves real problems, and gives people back their time.

We’ve all seen it. A model starts off doing the right thing, then goes down the wrong path. It wastes tokens, makes bad decisions, gets stuck, or keeps repeating work that clearly isn’t working. And before anyone notices, you’ve burned time, money, and compute.

AI agents are becoming increasingly capable, but the infrastructure around them has not caught up. Most agents still operate across fragmented tools, applications, memory systems and permission layers. AgentOS is built around a simple idea: if agents are going to perform real work, they need an environment designed for them.