Best practice 7
Make provenance and citation first-class¶
Practice¶
- Any answer an agentAgentA system built around a language model that carries out multi-step work: it plans, calls tools, reads the results, and decides what to do next. The model supplies the reasoning; the surrounding harness does the acting. Distinct from a chat model that answers one prompt at a time. gives should be traceable to its sources and open to audit.For
- W3C PROV-O (2013)
- ELIXIR AI strategy (2026) §5 references to primary sources; §10 reproducible research extended to AI
- Open and Sustainable AI in the life sciences (OSAI, 2025) reproducibility and reusability of AI outputs across the ecosystem
- NISO CREC (RP-45-2024) retraction status travels with citations
- Crossref and the Retraction Watch database (2023) open machine-readable retraction data
- Fabricated citations from LLMs (Walters & Wilder 2023) fabricated citations (evidence for a source trail)
- AI tools cite retracted papers (MIT Technology Review 2025) AI tools cite retracted papers unflagged
AgainstNone recorded - It should be clear what was done by a human and what by an agent.For
- CRediT contributor roles taxonomy (2022) contribution attribution
- ELIXIR AI strategy (2026) §4, §8: credit to experts and to underlying services where derived models overshadow them
- ICMJE and COPE on AI and authorship (2023) AI cannot be an author; disclosure required
AgainstNone recorded
Treat an agent answer with no source trail as unchecked. Keep the link between a result and its evidence, and mark what the agent produced and what you did, so the work can be checked and cited later. Disclose agent use where it matters (in a paper, a report, a review). You are still accountable for the judgement, whatever the agent found.
Make provenance part of the output, not an add-on. Return sources with each answer, detailed enough to verify, pass through citation and retraction status, and mark agent-generated content. Record enough of each run (model version, inputs, tool calls) that it can be re-examined. Machine-readable provenance is what lets others evaluate and audit your system.
Provenance comes before audit and trust. Require that agent outputs be traceable, that human and agent contributions be clearly separated, and that agent use be disclosed where results are published. Use that trace as the basis for independent evaluation where the stakes are high.
Reasons¶
Science depends on the path back to evidence: results are trusted because they can be checked. Without a path back to the sources, a claim cannot be checked, cited, or corrected, and a wrong answer that looks right cannot be told apart from a correct one. This is not hypothetical: studies find large fractions of LLM-produced citations are fabricated or wrong, and AI research tools have been shown to cite retracted papers without flagging them. It also matters to separate what a human did from what the agent produced, for authorship, accountability, and tracing errors. There is wide editorial consensus (ICMJE, COPE) that AI cannot be an author and that its use must be disclosed. A research agent has to pass citation and retraction status through to its output, or it will present withdrawn work as current.
Examples¶
- An agent returns a confident literature summary with no sources; a reader cannot tell a sound claim from a fabricated one, and later finds that one cited paper does not exist.
- Each answer instead carries references detailed enough to check the specific claim, and cited work passes through retraction status, so a withdrawn study is flagged rather than presented as current. The NISO CREC recommended practice and the open Crossref/Retraction Watch data make this checkable.
- An agent-written passage is pasted into a manuscript with no marking; a co-author cannot tell which text and which analysis were the agent's, and authorship and error-tracing get muddled.
- Human and agent contributions are labelled, in outputs and in anything the agent writes to, agent use is disclosed where the work is published, and the trace is machine-readable (for example W3C PROV, RO-Crate, or CRediT) so it can be checked automatically.
- The run is recorded: model version, inputs, and tool calls kept so a surprising result can be re-examined and, where possible, re-run.
- The agent supplies the traceable evidence and the person makes the call, remaining accountable for the judgement.
Sources¶
- ELIXIR TF Agentic AI: agenda and rolling best practice (2026), Best Practice item 6.
- Agentic AI in the higher-education system (2026), §3.3 (source traceability, retraction notices) and §4.1 (situated judgement is not automatable). Consistent with, grounding only.
- EU Expert Forum on Frontier AI (2026), §4.2.3 (evaluation and verification rest on traceability). Consistent with, grounding only.
- W3C PROV-O (2013) and CRediT (ANSI/NISO Z39.104-2022). Machine-readable provenance and contribution attribution.
- ICMJE / COPE on AI and authorship (2023). AI cannot be an author; use must be disclosed; humans remain accountable.
- NISO CREC RP-45-2024 and Crossref / Retraction Watch. The standard and the open data for propagating retraction status.
Change history¶
- 2026-07-27: Renumbered from BP06 to BP07 on inserting the new BP01 (match the method to the task).
- 2026-07-27: Rewrote Examples as concrete scenarios (actor, action, outcome), including anti-patterns; kept the labelled instances (NISO CREC, Crossref/Retraction Watch, PROV, RO-Crate, CRediT).
- 2026-07-26: Rewritten to add standards grounding (PROV-O, CRediT, ICMJE/COPE, NISO CREC, Crossref/Retraction Watch), fold in run-level recording (reproducibility) and disclosure of agent use, and cite the fabricated-citation and cite-retracted-work evidence as support.
- 2026-07-25: Created from the ELIXIR TF distillation (hook 7), grounded in the HFD paper and the EU Expert Forum entry.