Before & After
Traditional discovery vs. governed recursive intelligence
The same research question, worked two ways. Choose a scenario to see how a governed recursive-agent workflow reshapes each stage — without ever removing the human from the decision.
Interactive comparison
Pick a research scenario
Illustrative workflows for demonstration. Every FantomX output is evidence-linked and requires qualified human review.
Scenario — A research team investigates candidate variants tied to a disease phenotype and needs a defensible, reviewable shortlist.
Traditional workflow
FantomX recursive-agent workflow
Framing the question
Analysts manually scope the question and align on hypotheses across separate meetings and documents.
Framing the question
A planning agent decomposes the objective into sub-questions and proposes a structured evaluation plan for human sign-off.
Gathering evidence
Literature and datasets are searched by hand — slow, uneven coverage, and easy to miss relevant sources.
Gathering evidence
Retrieval agents assemble candidate evidence in parallel, each item traced to its source for later audit.
Reasoning & synthesis
A few specialists reason linearly; competing interpretations are hard to reconcile and rarely re-examined.
Reasoning & synthesis
Recursive agents evaluate competing hypotheses, critique their own reasoning, and converge on a ranked, evidence-linked shortlist.
Review & governance
Review depends on whoever is available; the reasoning trail is scattered across emails and notebooks.
Review & governance
Every step is logged with an audit trail and routed for qualified human review before anything is accepted.
What changes
The shape of the difference
A qualitative view of where a governed recursive-agent workflow shifts the research experience.
Comparative signals are directional and illustrative, not benchmark measurements.
See the recursive workflow in motion
Step into Mission Control to watch the pipeline run, or explore the intelligence graph behind it.