AUTHORITY + EXECUTION
Trace the deployment context
The target model relates a proposed agent deployment to authorization records, execution observations and the versions under review.
Controlled preview
Sample data
A proposed oversight information structure using static samples. No live upload, account flow, launch approval or regulatory judgment.
Agent Review & Accountability Oversight Application
Pridicta ReviewPack for agent deployment review.
The target model organizes external authorization, execution observations, human oversight, and incident/remediation records as reviewable evidence. This Controlled preview does not implement live integration, launch approval, or ReviewPack generation.
Make today's approval reconstructable and verifiable tomorrow.
Target model: the Pridicta ReviewPack is built on the HUATRA ProofPack evidence model in the shared HUATRA Accountability Core.
Target model: External Records → Canonical Evidence → Claim → Review → Snapshot → pack.
Decision ≠ Enforcement ≠ Execution. Evidence for human review. Review ≠ Approval.
In the target model, reviewers can inspect which evidence is present, which version they are looking at and what limitations remain. Pridicta does not decide whether an AI system should launch.
Relate external authorization, execution, and oversight evidence.
OVERSIGHT + INCIDENT
Keep attribution and gaps visible
The target model distinguishes attributed human approval or intervention from unsigned observations, incidents and remediation records.
REVIEW + LIMITATIONS
Keep open questions inspectable
Reviewers can inspect which evidence is present, which gaps remain and what questions require human judgment.
Human authority remains with the reviewer.
The target model organizes external authorization records, execution observations, human oversight, and incident/remediation records as reviewable evidence. Reviewers retain their decisions. This preview does not claim live Core capability or an active workflow integration.
Target model
Pridicta is an Agent Review & Accountability Oversight Application designed to organize agent-deployment evidence before human review.
Sample data
Public examples are fictional and do not represent a production customer, connected agent or completed review.
Human review
The target model lets reviewers inspect evidence, versions and limitations. Pridicta does not approve or deny launch.
Intended teams
AI assurance, internal audit, technology risk, AI governance, insurance and third-party risk teams reviewing agent deployments.
The launch decision belongs to people.
No automated regulatory approval.
No legal-sufficiency or automated-compliance judgment.
No verified production-customer or live-paid-pilot claim.
Evidence for human review. Review ≠ Approval.
Decision ≠ Enforcement ≠ Execution.
Pridicta supports human oversight. A review record does not establish that an AI system is safe to launch or that a legal obligation has been satisfied.
A review packet with its open questions visible.
Synthetic agent deployment · Sample data
CLAIM A · AUTHORIZATION
Sample: ALLOW at 10:00, attributed to a PDP reference and policy v17.
CLAIM B · ENFORCEMENT
Execution is observed in this fictional example; the observation is not independently signed.
COVERAGE GAPS
The sample does not establish complete evidence or prove what happened outside the listed observations.
REVIEWER QUESTION
Was authority revoked before execution?
Discuss a scoped pilot or review the planned options.
The public example uses sample data only. No customer material is accepted by this design.
Talk to us about a pilotSee pricingReview ≠ Approval.
Not legal advice.
Not compliance certification.
Not risk scoring.
No real uploads in controlled preview.
Agent deployment oversight
Authority, execution and oversight evidence can be scattered.
Authority is separated from execution
An agent deployment can leave authorization in IAM or a policy decision point, execution in runtime logs, and incidents in separate systems.
Attribution is difficult to reconstruct
Reviewers need the policy and evidence versions, attributed human oversight, and limitations that frame an agent action.
Incidents and remediation are disconnected
A readable chain can relate an incident, its evidence and proposed remediation without deciding whether a system should launch.
Evidence does not answer every question
The target model keeps coverage gaps and reviewer questions visible; observation is not a fact finding and integrity is not completeness.
Target model · ReviewPack
Eight bounded sections for human review.
01 · Scope & Boundary
Purpose, intended users, boundaries and deployment scope for the synthetic example.
02 · Principal & Authority
References to identity, policy-decision and runtime records that describe deployment context.
03 · Decision / Enforcement / Execution
Attributed source records and versions, separated from conclusions about their meaning.
04 · Claims & Evidence Bindings
Bounded statements link to evidence and retain their stated limitations.
05 · Verification State
A reviewer examines evidence and open questions; review is not approval.
06 · Human Oversight
Attributed human approval or intervention is distinguished from unsigned observations; a person assesses evidence and limitations.
07 · Incident / Remediation / Closure
Incident context, remediation ownership and closure evidence remain distinct; no legal, compliance, risk or launch conclusion is implied.
08 · Snapshot / Manifest
A bounded snapshot records the context presented for review; unobserved sources and unanswered questions remain explicit.
Sample data
Synthetic example: agent deployment review
A synthetic agent deployment review shows an authorization claim, an enforcement observation, coverage gaps and a reviewer question. Sample data.
Target model · Evidence chain
Relate evidence to a human review and bounded artifact.
External Records
Start from identity, policy-decision and runtime records that describe the agent deployment context.
Canonical Evidence and Claims
Relate versioned evidence to bounded claims while keeping unsigned observations and evidence gaps explicit.
Review, Snapshot, Artifact
Prepare context for human review and preserve a bounded snapshot; people retain the decision and its accountability.
Privacy and security
Calm control language before any live workflow.
OAuth identifies account
If account access is separately approved, OAuth identifies the account context. This public site does not start login.
Stripe handles payment details
If payment is separately approved, Stripe handles payment details. This public page does not activate checkout.
Entitlement controls access
Access must come from entitlement records. Public preview pages do not unlock a workspace or review workflow.
No real system materials
The controlled preview does not accept real AI system materials, identity documents, regulated data, or confidential files.
Enterprise pilots for organizations. Controlled self-serve access for small teams.
Enterprise pilot - contact us
Design partner and paid pilot programs scoped to your workflow, evidence requirements and reviewers. Quoted individually and agreed in writing.
Best fit: AI assurance, internal audit, technology risk, AI governance, insurance and third-party risk teams reviewing agent deployments.
Contact us at contact@huatra.ai. No self-serve checkout.
Controlled self-serve access
Planned pricing - not yet available for purchase
Self-serve plans cover individual access for small teams. Organization-wide pilots and evidence operations are scoped separately.
Approved accounts only during the controlled preview.
Best fit: Small teams preparing their first agent accountability review.
Monthly subscription
US$25 per month. Renews monthly. Cancellation takes effect at the end of the paid period.
Request accessYearly subscription
US$300 per year. Renews yearly. Cancellation takes effect at the end of the paid period.
Request access30-day one-time access
US$30 for 30 days. Does not renew.
Request access365-day one-time access
US$280 for 365 days. Does not renew.
Request accessFinal terms, renewal and refund policy will be published before purchase is available.
FAQ
Clear controlled preview boundaries.
Does Pridicta approve launch?
No. The target model organizes external authorization records, execution observations, human oversight, and incident/remediation records as reviewable evidence. People retain launch decisions; Pridicta does not decide whether an AI system can launch.
Can I upload real materials?
No. The controlled preview site is a static preview and does not accept real system materials.
Does the controlled preview certify compliance?
No. The controlled preview does not provide compliance certification, legal advice, launch approval, or risk scoring.
Is sandbox content fictional?
Yes. Public examples are labeled Sample data and are fictional.
Footer safety anchor
The Pridicta target model organizes external authorization records, execution observations, human oversight, and incident/remediation records as reviewable evidence. This Controlled preview does not claim live Core capability. No real upload, checkout, account creation, provider workflow, live package generation, launch approval, certification, or commercial launch is active from this site.