Superagents
One objective. An entire team of agents.
Superagents turn complex objectives into coordinated teams of specialized agents. They investigate, execute, and validate in parallel, dynamically adapting their approach as the work evolves.
01
Research doesn't follow a script
Research is rarely linear. New findings lead to new questions, promising ideas need further testing, and the direction of an investigation changes as evidence emerges. Most AI tools still require people to coordinate much of that process.
02
Agents that adapt as they work
Give a Superagent an objective and it assembles specialized agents to pursue it. They investigate, execute, and validate in parallel, adapting their approach as new information emerges, with every step open to inspection.
03
Autonomy without losing control
Run Superagents on your own infrastructure, choose your models, and define the rules they operate within. Every agent's work is backed by inspectable code, execution history, and results.
Context accumulates across sessions by default. Superagent owns its backlog and state, carrying what it learned into the next run, with no re-prompting required.
Autonomous Research Pod
