Use cases
What people put their AI teams to work on.
The same team of agents, in the same world, put to different kinds of work.
- AI agents for software teams: Put a team of AI agents on your codebase: they plan features, write code, review pull requests and file issues, and you watch every hand-off live.
- AI agents for research: A team of AI agents that read in the library, compare notes in meetings and write up findings, working in parallel while you watch and replay.
- Self-hosted AI agents for the enterprise: Run a team of AI agents on-premises, in your own VPC or fully offline with local models. Agents, memory, data and keys stay inside your network.
- Always-on AI operations: AI agents that keep working day and night: run on a schedule, reachable from Telegram, connected to your tools, with every hour replayable later.
- Agent evaluation and datasets: Study how AI agents move, cooperate and communicate in a physics-based world you design. Replay any session and export it for training and evaluation.
- AI agents and robots: Through ROS 2, the same UnderLife agents that work in your 3D world can see through and move real robots, on one network with people and agents.