Where "I don't know why it said that" goes away.

Every agent answer points back to the claims behind it: source, timestamp, confidence, currency. So you stop debugging vibes and start debugging facts.

See it working Security questionnaire example
Every answer tied to a source Conflicts stay visible Changes propagate automatically
86M biomedical claims / 1.3M/sec ingest / 30+ connectors / Apache 2.0 / Python + Rust
Agent query Should I auto-renew this enterprise contract?
Usage declining 18%
product-analytics · trailing 90d
Health green stale 41d
Gainsight · last set Mar 28
Pricing $120K vs $95K conflict
contract draft (May 4) vs CFO approval (May 6)
CSM note "churn risk"
Slack #cs-acme · 2 days ago
Answer

Do not auto-renew. Health score is stale (41d), usage is declining, pricing has an unresolved $25K mismatch, and the CSM flagged churn risk this week. Route to human review with all four signals attached.

The moments knowledge betrays you

Drift hits on a calendar.

Your agent does not fail loudly. It fails on the day someone outside your team looks - an auditor, a customer, a regulator. Four patterns we see every week.

Old information wins

A policy changes and the agent keeps citing last quarter's version. Retrieval is not the same as correctness.

Conflicts disappear

Two sources disagree. The model picks one without telling you. The contradiction stays buried until the customer asks.

Bad data spreads

One wrong source feeds forty-three downstream answers. Nobody notices until the source is withdrawn and nothing updates.

No explanation path

When a decision matters, "the model said so" is not an answer. Every reply needs a statement, a source, a timestamp, and a confidence.

Foundation

Watch an answer earn itself.

Four moments where the chain matters more than the text the agent retrieved.

Trace
Answer claim claim source

"Why did the agent say that?" Always has a real answer with sources, timestamps, and confidence.

Contradiction
$500K $450K surfaced

Two sources disagree. Both shown with timestamps. The agent doesn't pick one silently.

Propagation
Source revised 43 downstream answers flag

Policy moves, paper retracts, score flips. Every dependent reply lights up the same minute.

Drift detection
agent conf 0.94 · truth match 0.61

When agent confidence drifts from ground truth, the gap surfaces. No silent regressions.

Example

Security questionnaire agent

"Do you encrypt customer data at rest?" Three ways to answer. Only one survives a real audit.

Copy-paste
"Yes."
From last questionnaire

Last response said yes. Policy has changed twice. Nobody checked. Reviewer finds it.

RAG agent
"Yes, AES-256."
Closest retrieved chunk

Similarity wins. Audit doc contradiction silently dropped. Stale policy citation goes unflagged.

AttestDB agent
"Yes, AES-256."
1 stale citation flagged

Answer cites current policy + audit. April response on policy v1 flagged for reissue. Conflict visible.

Flow

When knowledge changes, your agents update.

The product value is not the first answer. It is what happens after the answer when reality changes.

  1. Ingest documents, tickets, policies, and prior work.
  2. Extract structured claims with provenance.
  3. Agents answer questions from those claims.
  4. A source is updated, corrected, or invalidated.
  5. Dependent claims are flagged or corrected automatically.
No stale answers. No hidden errors. No manual archaeology.
Why This Category

Same problem. Three answers.

Salesforce says ARR is $500K. Billing says $450K. Three database architectures handle the same conflict very differently.

Traditional DB
$450K
Last write wins

One row survives. The disagreement is invisible. Found out in next quarter's board deck, or never.

Vector DB
closest match
Similarity wins

One vector returned. The other isn't surfaced. No timestamp, no source, no conflict signal.

AttestDB
$500K $450K
conflict surfaced

Both claims preserved with timestamps and confidence. The conflict shows up where someone can resolve it.

Build agents you can trust.

AttestDB gives you the storage primitive for sourced, changing, contradictory knowledge. That is what enterprise agents actually need underneath them.