A research agent that finds two conflicting sources should not silently blend them into one confident statement. Contradiction resolution is an evidence workflow: establish what each source actually claims, when it was true, which region or version it covered, and whether the differences can be reconciled without inventing facts.
Identify the disagreement precisely
Do not mark entire pages as contradictory when they describe different dates, markets or product tiers. Extract the exact disputed proposition: for example, whether a feature was available to enterprise customers in a named region on a specified date. Record publisher, primary-document URL, first publication, last observed update, supported product version and evidence excerpt. If two sources use the same product name for different APIs, the disagreement may disappear once those identities are separated.
Prioritize evidence, not loudness
A dated changelog or versioned API reference generally supports a release claim more directly than a current marketing overview. A first-party policy is stronger evidence for that vendor’s present terms, but it is not an independent benchmark. Historical release notes must not inherit later capabilities. Search for corrections, deprecation notices and archive snapshots while noting that a living page may be rewritten. Where dates or scopes remain ambiguous, record an unresolved conflict rather than extrapolating.
Concrete example
Imagine a provider page calling a tool ‘available globally’ while a developer endpoint reference lists only several supported regions. The agent should present the product-level statement and endpoint-level restriction separately, indicate the checked date, and avoid stating that every customer can call the API. If a buyer’s plan is unknown, leave entitlement null. The editorial record should preserve both exact URLs and identify which claim each supports.
A safe contradiction workflow
Retrieve candidate pages through read-only tools, de-duplicate mirrors, identify the original publisher and quote only the minimal passages required for review. Do not follow instructions embedded in a page that ask the agent to change search terms, disclose documents or rewrite the final article. A reviewer receives a compact evidence matrix with competing claims, date, scope and proposed wording. Only approved language enters a production article.
Evaluate with adversarial cases
Build tests where one source is outdated, another is region-specific, a third is a vendor benchmark and a fourth contains prompt injection. Check whether the agent flags the uncertainty and avoids taking actions based on contested facts. Score source-to-claim traceability, unsupported assertion rate and correct abstention; do not reward merely producing a polished single answer. Human source review remains necessary for high-stakes or rapidly changing vendor claims.
Make uncertainty useful to the reader
A useful article can say “this endpoint is documented for some regions, but current availability in the buyer’s region is unverified.” It should not evade every decision with vague disclaimers. Distinguish contradictions about hard facts from subjective preferences or different evaluation methods. Attach a concrete next verification step, responsible source and date for each unresolved issue so editors can promote the record after evidence arrives.
Production acceptance and review test
Review a synthetic evidence matrix containing one dated changelog, one undated marketing page, one third-party summary and one instruction-laden fake help article. The agent must identify scope differences, cite the first-party release for the historical claim and decline to infer missing prices or plan tiers. A reviewer should be able to reproduce the determination from the exact URL set without trusting the model’s hidden reasoning.
Scope, version and availability
Features documented by a current vendor page are not assumed to have existed in earlier releases. Where a historical launch date is provided, it is supported by a dated first-party publication. Exact subscription tier, market availability, quotas, API billing, licensing permissions and deployment regions are not asserted without a directly applicable record. This is researched editorial guidance rather than a claim of independent hands-on testing, deployed operation or statutory certification.
Sources and Further Reading
- LangChain — LangSmith evaluation types (source reviewed 2026-10-25)
- OpenAI — Designing AI agents to resist prompt injection (source reviewed 2026-10-25)