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Xplore
Solutions · Error Prevention

Stop agent errors before anyone acts on them.

Your agents write reports, fill dashboards, make recommendations. How do you know the numbers are real, the sources exist, and nothing was cherry-picked? Xplore Integrity verifies every claim mechanically — hallucinations get caught at the source, not discovered in a board meeting.

The problem no one solved cleanly.

Agents hallucinate numbers

An LLM confidently reports "$28,000 exposure" — but the file says $1.4M. There's no trace of what was read or computed. You find out only when someone checks manually.

Sources get fabricated

The agent cites a URL that never existed, a snippet it never fetched, a date from a file it never opened. It looks right because it's well-formatted.

LLM-as-judge doesn't fix it

Checking one model with another shifts the problem — the judge hallucinates too, leaves no audit trail, and the error is indistinguishable from the original.

How it works

Three steps to verified agent output.

No rewrite of your agent code. Integrity wraps around what you already have.

1
Connect

Add our SDK (one wrapper), plug in a bridge for structured logs, or route through a gateway proxy. The agent doesn't change.

2
Verify

On every run, integrity checks fire automatically: source resolution, value grounding, arithmetic, completeness. 16 of 17 checks are fully deterministic.

3
Act on the passport

Every run gets an integrity passport — trust score, risk flags, failed checks. Gate publications, trigger reviews, or just give users the confidence signal.

What gets checked.

Four categories cover the full surface of agent errors — from invented sources to arithmetic mistakes.

Source validity

Every cited source is resolved against the observed zone. Did the agent actually fetch that URL? Does the file exist? Is the snippet really in the captured content?

Claim grounding

Values asserted by the agent must exist in the content of their cited source. "$28K" must appear in the bytes we captured from the system of record.

Arithmetic consistency

Derived numbers carry their formula. The verifier recomputes. Cross-widget totals must add up. Parts must equal the whole.

Completeness

Slot policies define what evidence types the task requires. Missing categories are flagged — not averaged away. Cherry-picking becomes structurally visible.

Integration

Works with your agents. Any framework.

Deeper integration means more visibility — and a higher trust cap. But even a lightweight connection gives honest, qualified results.

Your own code

SDK wrapper. Full I/O capture. Trust cap ×1.0

OpenAI / OpenClaw

Bridge on structured logs. Trust cap ×0.85

MCP / A2A agents

Gateway proxy. Trust cap ×0.7

Third-party / closed

Declared push + re-checks. Trust cap ×0.4

Built for regulated environments.

The integrity graph is the audit artifact regulators ask for — without separate documentation.

EU AI Act

High-risk systems require traceability of AI decisions. The provenance graph provides it by construction — every claim traced to its evidence.

SR 11-7 / Model Risk

Financial regulators require independent validation of model outputs. Deterministic checks on the observed zone are independent by construction.

GDPR Provenance

Every data point that feeds an automated decision is traced to its origin. Origin policies enforce "balance from CRM, not from web scrape."