Research workflows that do not lose the experiment.
Run simulations, discovery loops, and multi-stage analysis close to lab data, internal tools, and private compute. Preserve the work through long runs and ordinary failures.
Research is iterative. The runtime has to remember every turn.
A discovery loop can query databases, run predictions, score candidates, ask for review, and use the result to plan another round. A simulation may fan out before aggregating a result.
When a tool or worker fails late in a long run, the workflow needs a known recovery point—not another start from the beginning.
- 01
Parallel workflow graphs
Distribute logical workers across eligible runtime nodes, then aggregate their outputs into the next stage.
- 02
Multi-stage research loops
Define explicit stages where agents exchange artifacts, branch, repeat, and pause for review.
- 03
Persisted run state
Keep job metadata, events, and terminal state inspectable and recovery-aware after interruptions.
Start from a working research loop.
Keep infrastructure from becoming another research project.
Start beside private research data on one machine, then add capacity without redesigning the workflow around a general-purpose platform.