Oasis

Oasis is a workspace where people and AI agents work together with shared context, memory, policies, and approvals. The problem it goes after is the gap between an impressive agent demo and an agent you would actually trust inside your company. Most agents operate blind. They have no memory of what happened yesterday, no awareness of what the rest of the team is doing, and no guardrails on what they are allowed to touch, so every deployment either stays a toy or becomes a liability. Oasis puts humans and agents in the same environment, working from the same information and answering to the same rules, which is what the company means when it talks about giving agents situational awareness. The framing throughout is treating an agent like a colleague with a role, a memory, and a manager, rather than a clever autocomplete bolted onto a chat box.

The working unit is the room. Teams and agents share multiplayer rooms where work actually happens, and rooms can be scheduled so recurring jobs run on a cadence instead of waiting for someone to kick them off each time. A weekly pipeline review or a daily triage pass can live as a scheduled room that convenes on its own, with the relevant agents already present and already briefed. Memory is persistent rather than per-chat. Agents retain conversation history, records of past sessions, and standing facts that get applied consistently the next time they show up. That persistence is the whole point. An agent that re-learns your business every morning is a chatbot with extra steps, while an agent that remembers last week's decisions starts to look like a coworker.

Governance is the other half of the product, and it is treated as a first-class feature rather than a settings page. Organizations control which agents are allowed to run, what data and tools each one can access, and where approval gates sit in a workflow. Agents pause before designated actions and wait for a human to approve, edit, or reject, rather than operating fully autonomously. That structure also changes who can safely work with agents, since a teammate who would never audit a prompt chain can still review a proposed action and approve or reject it. Actions land in audit logs, and the platform's stated position is that agent activity inside a company's core systems should be both approvable and reversible. If you have ever watched an autonomous agent do something confidently wrong in a production system, this design reads like a direct response to that experience.

On the integration side, agents connect to business tools through MCP and direct APIs, so they can read from and act on the applications a company already runs rather than living in a silo. The agent library covers specialized roles across functions. Sales agents handle lead enrichment, CRM updates, and deal follow-ups. Support agents do ticket triage and knowledge-base grounded replies. Finance agents work reconciliations, accounts payable exceptions, and invoice processing, and there is further coverage for HR policy queries and operations work like pipeline hygiene, expense processing, and compliance checks. Every plan includes access to the full agent library and multiplayer rooms.

It fits teams that want agents doing real work in real systems with real oversight, from small teams trying their first agent to larger organizations that need SSO, admin controls, and audit trails before anything touches production data. The plan structure mirrors that ramp, since the governance features that matter most at scale, policies, admin controls, exports, and audit logs, arrive at the Pro tier. What sets it apart is the emphasis on verification over assumption. The platform's framing is that agents should validate data rather than guess, report their reasoning and actions explicitly rather than operate as black boxes, and roll out in stages behind approval gates rather than being switched on all at once. Plenty of products will run an agent for you. Fewer are built around the question of whether you can check its work and undo it.

Access is freemium. The free plan costs nothing and covers up to 3 seats, 10 integrations, and 10 scheduled rooms a month, limited to open source models. Starter is $19 a month for up to 10 seats, 25 integrations, 20 scheduled rooms, and all models with a pay-as-you-go option. Pro is $199 a month for up to 50 seats, 50 integrations, and 50 scheduled rooms, and adds policies, admin controls, exports, SSO, and audit logs. Enterprise is custom priced with unlimited seats, data residency, and dedicated support. Oasis states it does not train on your data on any plan, and it backs purchases with a no-questions refund promise.