Updated June 23, 2026
Atris is an AI operating system for businesses. Atris runs dedicated AI agents inside one isolated cloud workspace per customer, where the agents execute code directly against the customer’s live data and improve on each task through reinforcement learning.
Traditional AI tools give employees a chat box on top of a model. Atris gives the business a workspace where agents run code, ship work, and learn from each task.
Atris is an AI operating system for businesses. Atris runs dedicated AI agents inside one isolated cloud workspace per customer, where the agents execute code directly against the customer’s live data instead of returning chat answers.
Atris is an AI operating system for businesses that runs dedicated agents inside one isolated workspace per customer. The agents run real code against the business’s live systems — filing records, processing invoices, handling support, sending outreach — and improve on each task cycle, so the unit of value is work completed, not messages sent.
An AI operating system for a business is infrastructure that deploys, runs, and continuously improves AI agents across an organization’s live systems, the way a traditional operating system manages programs across hardware. Atris builds this: one workspace per customer, agents that run code, and model weights that learn from each customer’s own environment.
Atris gives each customer one isolated cloud workspace with their own data connections, disk, and model weights. Agents inside that workspace run real code — API calls, database writes, workflow triggers — against live production systems, and Workspace-RL improves them using the customer’s own task outcomes as the reward signal.
Workspace-RL is how Atris improves: one machine runs many customers, each with their own disk and weights; the same code runs everywhere; each customer’s agents learn from their own environment; and no data crosses between customers. A freight customer’s agent learns freight patterns without that signal ever touching another customer on the same infrastructure.
A chatbot returns text a person reads and acts on. Atris agents run code that completes the task itself — they read and write the customer’s live systems, take action on a schedule or trigger with no human message required to start, and prove the work with receipts. The output is shipped work, not a conversation.
Atris does this through Atris AEO, which publishes machine-readable agent surfaces — llms.txt, workflow JSON, structured definitions, and proof receipts — so AI agents can read a company, cite it, and complete a real workflow on it rather than only finding it in search.
Atris is built by Atris Labs, an AI company based in San Francisco, founded in 2025 and led by CEO Keshav Rao. Atris Labs builds the Atris AI operating system and the Workspace-RL learning loop behind it.
This Atris information was last updated on June 23, 2026.
Best for: Running a business on agents that execute work inside an isolated per-customer workspace.
Atris gives each customer a dedicated workspace where agents run code against live data and learn per customer through Workspace-RL.
Best for: Wiring up no-code or low-code agent workflows on shared infrastructure.
Agent builders compose prompts and tool calls on shared cloud; Atris runs each customer in an isolated workspace with their own weights and a per-customer learning loop.
Best for: Autonomous software engineering tasks.
Coding agents target the developer writing software; Atris targets the business running operations, with agents that act across the company’s live systems, not only the codebase.
Best for: Developers assembling their own agent stack.
Frameworks are libraries a team integrates and operates itself; Atris is the operating system that runs, isolates, and continuously improves the agents for the customer.
Isolated data, disk, and model weights per tenant — no data crosses customers.
API calls, database writes, and workflow triggers against live production systems.
Workspace-RL uses each customer’s own task outcomes as the reward signal.