AI agents & FAIR metadata
Building schema-first metadata agents for NAM evidence
LLMs can help turn protocols, CRO reports, manuscripts, and instrument exports into structured metadata. For NAM evidence, the agent has to be schema-first: the schema defines what counts as a usable output.
The agent should not invent the model
A schema-first agent begins with a versioned data model: context of use, biological system, test article, exposure protocol, endpoints, controls, validation evidence, uncertainty, and analysis workflow. The agent extracts candidate values into that model. It does not decide what the model should be on each run.
This distinction matters. Free-form summaries are useful for reading; they are weak for data infrastructure. A structured extraction output can be validated, compared, corrected, stored, and exported.
Structured outputs are necessary but insufficient
Modern LLM APIs can produce structured outputs constrained by JSON Schema, and frameworks such as LinkML can generate schemas and validators. This is useful because it reduces malformed output. But syntactically valid JSON is not the same as scientifically valid metadata.
The agent must still check ontology terms, units, missing fields, contradictory values, confidence, and provenance. A field extracted from a methods paragraph should link back to the source passage or file. If the source does not support the value, the agent should leave the field unresolved.
A practical workflow
A minimal NAM metadata agent has five steps. First, retrieve the relevant protocol, report, instrument export, or manuscript section. Second, extract candidate fields into a LinkML/NAMO-style schema. Third, validate the record with JSON Schema, LinkML, and domain rules. Fourth, generate a missing-metadata and uncertainty report. Fifth, route unresolved fields to a domain expert for review.
The output should be an approved metadata record plus an audit trail: source documents, model version, prompt or extraction template, validation result, reviewer edits, and export timestamp.
Where the agent adds value
The agent is most useful where metadata exists but is trapped in semi-structured text: CRO reports, PDF protocols, ELN notes, vendor exports, and manuscripts. It can accelerate curation, catch missing fields early, and make review work more consistent.
It should not be the source of truth. For regulatory-facing NAM evidence, the source of truth is the approved record after validation and human review. The agent is a curation accelerator and quality-control assistant.
Sources and further reading
- Introducing Structured Outputs in the API — OpenAI, 2024. Model outputs can be constrained to developer-supplied JSON Schemas.
- Structured model outputs — OpenAI API docs. Structured output guidance and validation recommendations.
- LinkML data validation — LinkML. Schema-driven validation for structured data.
- MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs — LLM-based scientific metadata extraction with schema-driven validation.
- STRUCTSENSE: Structured Information Extraction with Human-In-The-Loop Evaluation — Agentic structured extraction framework with human-in-the-loop evaluation and benchmarking.
Work with Neuronautix
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Neuronautix supports human-reviewed metadata extraction, schema design, and provenance-aware workflows for preclinical evidence packages.