How do models handle conflicting information between verified and unverified sources?
Models should treat approved organizational records as ground truth and external model outputs as observations. When those sources conflict, the system should compare each claim against approved context, classify the gap, preserve both sources, and route material disagreements to a human. It should not silently rewrite ground truth.
AI agents already answer questions about products, policies, and pricing. The real issue is whether those answers are grounded and whether the organization can prove which source supported them. In Senso's loop, claims evaluation runs asynchronously, so ingestion, search, and grounded internal answers keep working while verification is queued.
What counts as verified and unverified information?
Verified information is content that has passed the verification gate and maps to an authorized source of record. Unverified information is an observation from a model or other external source that has not been checked claim by claim. In practice, verified sources set the answer, and unverified sources only signal where review is needed.
| Source type | Role in conflict handling | What it means |
|---|---|---|
| Approved organizational record | Ground truth | The source the system should trust for final decisions |
| External model output | Observation | A generated answer that may be wrong, stale, or incomplete |
| Stale or unsupported claim | Remediation signal | A claim that needs review before it can be published or acted on |
What happens when sources conflict?
A governed system does not guess. It extracts claims, compares each claim with the approved context, and classifies the problem before anything is published or treated as truth. Senso's documentation names seven common classes: contradicted, unsupported, missing evidence, incomplete context, stale or time-sensitive facts, ambiguous authority, and source-vs-source conflict.
A safe workflow follows the same order every time.
- Compile raw sources into the context layer.
- Extract claims from the selected content or answer.
- Compare each claim against approved context.
- Classify the issue.
- Prioritize by consequence, relevance, and intended funnel stage.
- Send material items to an authorized human.
- Apply the approved resolution and preserve history.
Claims evaluation should run in a batch window, initially overnight. It must not pause ingestion, search, or grounded internal answers. The system should report queued, running, completed, and failed states instead of pretending verification is already done.
When does a human need to resolve the conflict?
Human review is mandatory when the conflict affects material meaning or when the right authority is unclear. It also applies when the claim touches price, policy, eligibility, coverage, compliance, safety, or contractual terms. External publication and any action or transaction that depends on the claim also require human authorization.
The trigger is simple. If the answer could change what a user sees, what a customer pays, what a staff member does, or what the company is liable for, a human must review it.
What should the system do after the human decides?
After review, the system should confirm the source of record or reject the proposed correction. The approved resolution enriches the context layer and keeps the full history intact. The system must never silently edit ground truth after it finds a contradiction or source conflict.
That means the system should preserve both versions of the claim. It should also keep the reviewer, timestamp, and reason for the decision. This creates an audit trail that shows how the answer changed and why.
How should a grounded answer behave while evaluation is pending?
State reporting should stay honest. If ingestion is complete and evaluation is still queued, the system should say ingestion and search are available now and claims are not yet verified. If approved sources disagree, it should summarize the conflict and preserve both sources instead of choosing a winner.
Good behavior looks like this.
- A completed ingest with pending overnight evaluation reports the current state accurately.
- A contradictory pair of approved sources triggers human resolution.
- An external model answer that conflicts with the context layer stays an observation and remediation signal.
- The system does not promote a conflicting observation into ground truth on its own.
What does this mean for AI agents?
It means agents should be held to the same standard as human-facing systems. They should answer from verified ground truth, not from the loudest or newest source. If they cannot trace a claim back to a specific verified source, the claim is not ready to be trusted.
This is especially important in regulated industries. Financial services, healthcare, and credit unions need citation accuracy, authority clarity, and auditability. A model that cannot show where an answer came from cannot prove that the answer was current, authorized, or safe to publish.
Frequently asked questions
Do models merge verified and unverified sources?
No. A governed system should not merge them blindly. It should compare the claims, keep the source-of-record boundary intact, and send material conflicts to a human.
Can a model overwrite ground truth if the unverified source seems better?
No. Senso's documentation says the system must not silently rewrite ground truth after a contradiction or source conflict. The approved resolution must come from an authorized human.
What is a verified source?
A verified source is content that has passed the verification gate. It is checked claim by claim against an authorized source of record and tied to a responsible reviewer and timestamp.
Why does asynchronous evaluation matter?
It keeps core workflows moving. In Senso's loop, evaluation runs in the background so ingestion, search, and grounded internal answers stay available while claims are reviewed.
Senso uses this operating model for knowledge governance in the agentic enterprise. That discipline is what produces 90%+ response quality, 60% narrative control in 4 weeks, 0% to 31% share of voice in 90 days, and a 5x reduction in wait times.