Agentic Scientific Workflows: From Search to Traceable Decisions
A working model for scientific agents that retrieve evidence, plan the next step, preserve uncertainty, and leave a decision trail.
Scientific work rarely fails because one answer is missing. It usually fails because the path from question to evidence to decision is unclear.
That is why I think scientific agents should be designed as workflow systems, not just chat interfaces. A useful agent should help a scientist or engineer do four things:
- Frame the question tightly enough to search.
- Retrieve evidence from a known public or internal corpus.
- Preserve uncertainty instead of flattening it into confidence.
- Leave a trace that another person can inspect.
For AI for Science, the trace matters as much as the answer. If an agent suggests a molecule, a formulation, a paper, or an experimental next step, the user should be able to see where the suggestion came from and what assumptions were used.
My current ScientificLoop direction is built around that pattern: retrieval, tool use, evaluation, and human review. The agent should be able to answer, but it should also route the visitor or researcher to the next useful action: read a source, compare candidates, inspect a trace, or refine the question.
The practical design goal is modest: make scientific navigation less brittle. The long-term goal is stronger: turn repeated expert workflows into evaluated, auditable agent systems.