Connect.
Bring approved evidence and operational context into a defined decision scope.
When one fact changes, Guardian Veritas computes the blast radius across tracked pending AI agent actions, holds the affected set, and leaves unaffected work eligible to proceed.
Reference demonstration · Controlled pilot evaluation
Illustrative dependency model, not a live system.
An agent can follow yesterday’s instructions perfectly—and still take the wrong action today.
A customer opens a dispute. A source is corrected. An approver changes the scope. The important question is no longer just “Was this allowed?” It is “Does the permission still hold, given what changed?”
Guardian Veritas is an evidence-aware control layer: it identifies dependent actions, rechecks their authority, and preserves a record a person can inspect.
Bind decisions to the evidence and policy context that support them.
Hold affected actions instead of treating every change as a global stop.
Keep a bounded record of what changed, what was checked, and why.
Follow a fictional accounts-receivable workflow. Change the evidence, watch the affected reminder pause, then revalidate its permission.
The reminder’s permission depends on this invoice’s dispute status.
The current evidence supports preparing a reminder for Invoice A-104.
This separate record has no dependency on A-104’s dispute evidence.
The original evidence supports the reminder. Both simulated paths remain eligible.
Eligible to prepare a reminder.
No open dispute. Policy: do not prepare collection reminders while a dispute is open.
Applies to A-104 only. A synthetic decision receipt—not a guarantee of correctness or a production audit.
This is an illustrative, synthetic workflow—not a live ERP integration. No customer records, collection messages, payments, or production systems are connected. The demonstration does not establish production assurance.
The simulation above is an explainer. The working engine runs end to end in local, synthetic evaluations.
In the recorded accounts-receivable evaluation, changed evidence held five affected actions while two unaffected actions continued. Partial revalidation and fresh approval produced three mock effects with three independently verified signed receipts.
The latest recorded AR regression passed 1,958 tests with no failures and eight optional skips. These results cover the evaluated software snapshot, not live customer integrations.
Local execution is demonstrated. On-premises and fully air-gapped deployments require environment-specific validation. No real payments, customer messages, or live ERP actions are established by these results.
U.S. provisional patent filed October 2026.
Six complementary responsibilities organize the Guardian Veritas architecture—from approved context to a reviewable decision.
ARCHITECTURE DIRECTIONBring approved evidence and operational context into a defined decision scope.
Check whether the proposed action still satisfies the relevant authority and policy conditions.
Map how evidence supports a decision so a change can be followed to the affected action.
Isolate suspect or stale inputs and hold dependent actions within configured boundaries.
Support reviewed repair and revalidation. Compensation depends on what the connected system permits.
Capture the evidence, policy context, and outcome in a bounded, inspectable decision record.
These are six responsibilities of the engine architecture. Implementation and validation maturity vary by module; production integrations are scoped and validated per customer. “Recover” does not mean universal undo; “Proof” does not mean universal proof of safety or correctness.
The ambition is broader than one workflow: accountable AI-assisted work across enterprise systems, with human authority and system limits kept visible.
Evaluate evidence-sensitive workflows such as payment release, receivables review, and reconciliation. Current execution evidence uses synthetic workflows and mocked effects.
Explore how changes in one approved source should affect dependent actions elsewhere.
Keep review, escalation, and explicit authority in scope for consequential enterprise actions.
Cyber Warfare Operations Officer, U.S. Space Force. Director, Human-AI Teaming Lab, Space Systems Command.
16 years in cybersecurity engineering across Army, Navy, Microsoft, and Walmart. Harvard Kennedy School MPA candidate.
Bring a bounded workflow, the evidence it depends on, and the conditions that should stop it. Let’s define what a responsible evaluation would need to show.
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