MyDot Consultancy

Featured image: AI-generated conceptual illustration. Sources checked 26 October 2026.

Finding a paper is not the same as reading it, and reading an abstract is not the same as evaluating the study. An evidence-discovery agent should keep those stages separate. Its first job is to produce a traceable candidate set for review, not to manufacture a definitive research conclusion from bibliographic metadata.

This distinction matters for business teams exploring a technical question. A title can sound relevant while the population, setting or outcome differs from the decision at hand. A polished summary can hide that mismatch unless the workflow records what evidence was actually inspected.

Frame the question before searching

Imagine a hypothetical operations team investigating whether a particular training approach improves error detection in its workflow. Define the population, intervention, comparison and outcome in plain language. Record which settings would be relevant and which would be too different to support the business decision.

The agent can help develop search terms and synonyms, but the team should review the question and inclusion criteria. Otherwise the search can drift toward whatever literature is easiest to find. A clear question also helps explain why a candidate was included or excluded.

Keep the initial scope modest. A targeted discovery exercise is different from a systematic review. Do not describe the result as comprehensive unless the search strategy, coverage and review process justify that claim.

Use metadata to identify candidates

Crossref’s REST API documentation describes access to deposited scholarly metadata, including identifiers and other bibliographic information. That makes it useful for finding and resolving candidate works. It does not mean every returned record contains the full paper or enough information to assess the findings.

For each candidate, preserve the DOI where available, title, authors, publication information, query that found it and retrieval date. Normalize identifiers carefully and retain the original record for diagnosis. Similar titles or author names can otherwise lead to accidental merging of distinct works.

Do not infer quality from metadata richness alone. A complete record can describe a weak or irrelevant study, while a sparse record can point to useful research. Metadata supports discovery and identification; substantive appraisal requires inspecting the work.

Label the level of evidence inspected

Use explicit statuses such as metadata only, abstract reviewed, full text reviewed and methods appraised. A candidate with only a title should not receive a detailed findings summary. If an abstract is available, summarize only what it supports and label the limitation.

When full text is unavailable, provide the publisher or repository link and a lawful access route. Do not bypass access restrictions or assume that a metadata API grants reuse rights to the paper. Crossref also notes that some abstracts may carry copyright, so avoid treating every returned text field as unrestricted material for republication.

The agent can prepare a reading queue ordered by apparent relevance, but that ordering is a proposal. A domain reviewer should inspect the criteria and sample excluded candidates, especially when the decision is important.

Extract claims only after inspecting the source

A claim ledger should link each substantive finding to the inspected section, table or figure. Record the study setting, sample, outcome definition and limitations that affect interpretation. Do not flatten a conditional result into a universal recommendation for the business.

Separate what the authors report from the agent’s inference about applicability. A study in a laboratory setting may suggest a question worth testing in operations, but it does not establish that the same effect will occur in the company. Label that transfer as an inference and identify the missing evidence.

Use a human reviewer for methodological judgments and consequential recommendations. The agent can organize evidence and highlight questions, but it should not convert a literature scan into medical, legal or other regulated advice without the appropriate expertise and scope.

Check the current status of the work

Research records can be corrected, updated or retracted. Crossref’s Crossmark documentation describes a way to inspect reported content updates. It also cautions that the presence of Crossmark is not itself a guarantee. Treat status checks as another evidence source, not a universal certification.

Record the status checked and date. If an update changes a result used in the draft, flag the affected claim for review. Do not simply remove the paper without explaining why, because the team may need to understand how the evidence base changed.

The absence of an update signal should not be described as proof that no issue exists. Coverage and reporting vary. A responsible summary states the checks performed and their limits.

Return a discovery packet people can audit

• The research question and inclusion or exclusion criteria.
• The search terms, sources, filters and date of the search.
• Deduplicated candidates with stable identifiers and access links.
• The level of source inspection for each candidate.
• Claim-level notes only where the inspected material supports them.
• Update checks, limitations and questions for a domain reviewer.

Include excluded candidates when the reason is important to coverage. A short explanation such as wrong population or commentary rather than empirical study makes the process more transparent. Avoid retaining unnecessary full text when a citation and review note are sufficient.

Test citation integrity and restraint

Give the workflow a mix of similarly titled works, incomplete metadata, unavailable full text and a corrected publication. Check whether it resolves identities, preserves uncertainty and refuses to invent findings. Evaluate both relevant retrieval and unsupported-claim rates.

Costs include search requests, full-text acquisition through authorized routes, extraction and expert review. An agent may add little value to a small set of already known papers. It is more useful when discovery and record organization are repetitive, while humans retain responsibility for interpreting the evidence.

The best evidence-discovery agent makes the boundary between finding and knowing visible. It gives the team a better reading list, clearer provenance and fewer invented conclusions. That is a stronger contribution than a confident literature narrative built from titles the system never read.

Sources and further reading

Crossref: REST API. Deposited scholarly metadata and abstract-rights caveat; checked October 26, 2026.

Crossref: Crossmark. Reported updates and explicit limits of Crossmark as a guarantee; checked October 26, 2026.