The missing execution layer for AI agents.
SageOS governs what agents can do at the exact moment they execute — enforcing permissions, sandboxing runtime actions, and producing audit-first evidence.
🌍 The Vision (Big, but grounded)
AI agents are rapidly moving from experiments to real actors in the world — reading files, modifying systems, operating machines, and running workflows.
But there is a dangerous gap: Who governs agents at the exact moment they execute?
SageOS exists to solve that. The vision:
- AI agents work autonomously without becoming unsafe
- Humans trust agents to operate even offline
- Enterprises deploy agents without fear of runaway behavior
- Regulation is enforced by architecture, not policy PDFs
🚨 The Problem No One Fully Solved
Today’s AI platforms focus on prompts, orchestration, workflows, and cloud dashboards.
But when an agent actually executes — opens a file, scans a folder, runs a task, touches a device — most systems:
- cannot stop it mid-run
- cannot guarantee least privilege
- cannot prove what happened after the fact
- fail when the network is down
This creates security risk, compliance risk, operational fear, and human mistrust.
🧠 What SageOS Is Building
🔐 Governed Execution
Every agent action is permission-checked, scope-limited, auditable, and stoppable. Rules are enforced at runtime, not just at design time.
🛑 Kill Switches (Real, Not Cosmetic)
Tasks and agents can be stopped before execution, killed during execution, or halted globally in emergencies.
📜 Audit-First by Design
Every action produces timestamped logs, agent & organization identity, execution context, and immutable history. This enables compliance readiness, forensic review, and regulator trust.
🧱 Sandboxed Runtime
Agents do not touch the real world directly. They must go through governed tools, controlled execution paths, and deterministic limits (time, size, scope).
🌐 Works Offline & Online
SageOS does not depend on the cloud to enforce safety. Even without internet: rules still apply, limits still hold, and agents cannot escape governance.
🤖 Multi-Agent Future (What We’re Building Toward)
Designed for multiple agents, parallel execution, shared but isolated environments, and human-in-the-loop approvals — machine-speed execution with human oversight.
- Fleets of agents per organization
- Agents coordinating safely
- Agents operating machines, data, and systems
- Human approvals for sensitive steps
- Isolation between agents and tenants
- Replacing unsafe manual workflows
🧩 Where SageOS Fits
SageOS is not another LLM, another chatbot, or another workflow builder. SageOS sits below agent frameworks and above hardware.
It complements cloud orchestration platforms, enterprise AI tools, and agent frameworks — by governing execution itself.
- another LLM
- another chatbot
- another workflow builder
- cloud orchestration platforms
- enterprise AI tools
- agent frameworks
Governance is enforced by runtime design — not policy PDFs.
🚀 Current Status
Implemented
- Core runtime implemented
- Governed execution proven with live demos
- Kill switches and audit logs working
- UI demo showing real enforcement
In progress
- Actively expanding task types
- Validating with design partners
- Hardening policies & tool boundaries
To become the default execution safety layer for AI agents —
across laptops, servers, edge devices, and future autonomous systems.
A boring foundation. That enables extraordinary systems.
📩 Get in Touch
SageOS is looking for early adopters, design partners, advisors, and early-stage investors.