Summary
Enable external AI agents and autonomous research pipelines to programmatically invoke BioAnchor as a validation module.
Planned Features
- Agent-callable API — structured input/output interface for programmatic MCMC validation requests
- Batch validation mode — process multiple hypotheses/datasets in a single call
- Confidence scoring schema — standardized JSON output of reliability scores consumable by downstream agents
- Webhook / callback support — notify agents when long-running MCMC analyses complete
Example Use Case
An autonomous drug discovery agent generates candidate compound hypotheses → calls bioanchor.validate() with dose-response data → receives structured reliability scores → archives results on-chain + Arweave → proceeds or discards based on confidence threshold.
Design Notes
# Proposed interface sketch
from bioanchor import AgentValidator
validator = AgentValidator(
storage_backend="arweave",
chain="sui", # on-chain verification
)
results = validator.validate_batch(
datasets=[dataset_1, dataset_2],
model_spec="hill_equation",
callback_url="https://my-agent.example.com/webhook"
)
# Returns: List[ValidationResult] with reliability scoresRelated
- Depends on: v0.2.0 (on-chain), v0.3.0 (storage abstraction)
- See also: ROADMAP.md
Summary
Enable external AI agents and autonomous research pipelines to programmatically invoke BioAnchor as a validation module.
Planned Features
Example Use Case
An autonomous drug discovery agent generates candidate compound hypotheses → calls
bioanchor.validate()with dose-response data → receives structured reliability scores → archives results on-chain + Arweave → proceeds or discards based on confidence threshold.Design Notes
Related