How an AI agent can combine Life Science Research evidence synthesis with Exa source discovery to produce a literature brief with sources, caveats, and follow-up searches
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Life Science Research context and matches it with Exa, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent combine Life Science Research evidence synthesis with Exa source discovery to produce a literature brief with sources, caveats, and follow-up searches?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A Life Science Research and Exa literature discovery agent broadens source discovery while keeping scientific synthesis structured. Life Science Research supplies domain-specific databases, synthesis patterns, and evidence caveats, while Exa finds relevant web sources, papers, preprints, datasets, and institutional pages.
When to use this workflow
Use it for early research scoping, variant investigation, target reviews, hypothesis generation, or literature searches where discovery needs domain-aware caveats.
How Life Science Research and Exa give the agent context
Connect both plugins and define the research question, entity, variant, pathway, target, or disease area. Exa should expand the source set; Life Science Research should evaluate evidence quality and organize findings around the biomedical question.
Example starter prompt
Use Exa to discover relevant sources for this biomedical question, then use Life Science Research workflows to synthesize evidence, label caveats, and prepare a cautious literature brief with follow-up searches.Suggested workflow steps
Start with the scientific question and inclusion criteria. Have the agent discover sources with Exa, identify peer-reviewed vs. non-peer-reviewed material, check domain databases where appropriate, and group findings by evidence strength.
Expected handoff
Ask for source links, evidence strength, contradictions, database findings, caveats, preprint labels, weak evidence warnings, and recommended follow-up searches.
Questions this workflow answers
Can an agent find the most relevant recent papers for a biological question and separate peer-reviewed evidence from preprints and secondary summaries?
Yes. Define the research question, population or organism, intervention or exposure, outcome, study types, date range, and exclusions. Exa can discover current web sources and publication pages, while the life-science research context shapes the scientific inclusion criteria. The agent builds a candidate literature set before attempting synthesis.
Each record should preserve title, authors, publication venue, date, DOI or stable link, publication status, study design, population or model, and why it matches. A preprint remains labeled as a preprint. A press release or review can help discover primary studies but should not be counted as the underlying experimental evidence. Duplicate versions and updated manuscripts need one linked record.
The agent can screen titles and abstracts against the supplied criteria, but uncertain exclusions should be visible. It should not infer methods or outcomes that are absent from the accessible text. Retractions, corrections, small samples, surrogate endpoints, and conflicts between studies belong in the evidence notes rather than being smoothed away.
The handoff gives included candidates, exclusions with reasons, source links, evidence types, gaps, and next search terms. A qualified researcher validates the search strategy and reads the primary material before drawing conclusions. The agent accelerates discovery and provenance tracking; it does not replace database-specific systematic search methods or expert appraisal when the question requires them.
Search coverage should retain the exact query concepts, date searched, sources, language or publication filters, and stopping rule. Citation chaining can identify older foundational work and newer papers that use different terminology, while duplicate handling links preprint, accepted manuscript, and final publication. The agent should flag inaccessible full text and abstract-only screening because those limitations affect what can safely move into synthesis.