AGICOMPLY
Client LoginBook Founder Triage
AUTHORITATIVE ASSURANCE BOUNDARY

What AGICOMPLY establishes — and what it does not.

AGICOMPLY treats AI assurance as an evidence-engineering problem. The $3,500 ATO Readiness Baseline organizes supplied evidence for one defined AI system and one review context without overstating certification, authorization, legal scope, runtime monitoring, or control effectiveness.

CANONICAL DESCRIPTION

The precise description search engines, buyers, and reviewers should rely on.

AGICOMPLY produces a bounded, point-in-time AI evidence package. The engagement records supplied artifacts, content identities, accepted evidence relationships, limitations, gaps, remediation priorities, and chain-of-custody properties so a procurement, assurance, security, or authorization reviewer can reconstruct the current evidence position.

01

What AGICOMPLY establishes

A bounded evidence position

The Baseline covers one defined AI system, one agreed evidence population, and one review context at a stated point in time.

Artifact and package identity

SHA-256 content hashes distinguish supplied artifact versions and support later comparison of an issued package manifest.

Attributable evidence relationships

Candidate mappings can assist organization, but affirmative support in an issued package remains tied to human acceptance, rationale, authority, and limitations.

Explicit gaps and conditions

Missing, weak, stale, contradictory, or unresolved evidence is preserved in the Gap Register rather than converted into a broad compliance claim.

Reviewer reconstruction

The package is designed so another reviewer can examine what was supplied, what was accepted, what remained open, and which package version was issued.

02

What AGICOMPLY does not establish

An Authorization to Operate

Only the responsible authorizing organization can issue an ATO. AGICOMPLY prepares evidence used in readiness, procurement, and authorization review.

Legal applicability or certification

AGICOMPLY does not issue legal opinions, determine every applicable obligation, certify compliance, or represent government approval.

Complete model or training provenance

The Baseline evaluates the evidence supplied within scope. It does not independently discover every training source, parameter, dataset, or dependency used by an AI system.

Runtime data lineage or continuous monitoring

Content hashes identify reviewed artifacts. They do not establish every item of data the operating AI system touched or provide continuous runtime assurance.

Control operating effectiveness

A relevant artifact may support a design or implementation statement without proving that a control operated effectively over time.

03

The five outputs of the ATO Readiness Baseline

The fixed-scope Baseline produces five outputs, not four.

  1. 01

    Evidence Inventory

    What supplied artifacts exist, who owns them, which system they describe, and how each record is identified.

  2. 02

    Mapping Summary

    Which accepted evidence relationships support defined reviewer requirements, including rationale and limitations.

  3. 03

    Gap Register

    Which claims or review questions remain missing, weak, stale, contradictory, conditional, or unresolved.

  4. 04

    Ordered Remediation Plan

    Which evidence actions should occur first based on procurement and review dependencies.

  5. 05

    Chain-of-Custody Statement

    Which integrity, attribution, package identity, review-history, and change conditions apply to the issued evidence position.

04

What chain of custody means here

AGICOMPLY uses content identity, append-only review history, package manifests, reviewer attribution, accepted mapping records, and package versioning to support evidence integrity and later reconstruction.

  • It can show: which supplied artifact version entered the package, which package version was issued, and whether later evidence differs from the issued manifest.
  • It cannot show by itself: every runtime data interaction, every training source, or that the originating AI system remained unchanged in every environment.
  • When evidence changes: affected relationships can move to reverification required rather than continuing silently as current support.
05

How federal and framework references are used

NIST AI RMF and NIST SP 800-53 provide recognized governance, security, and privacy language. OMB 2025 AI governance and acquisition memoranda, including M-25-21 and M-25-22, can flow into agency and prime-contractor evidence requests.

These references inform evidence organization and reviewer traceability. They do not mean every requirement applies to every vendor, nor do they convert the Baseline into a certification or authorization decision.

EXAMINE THE METHOD

Verify the claim before relying on it.

Review the Public Assurance Register, Package Verifier, Evidence Standard, and Procurement Vault, or apply the method to one defined AI system through the ATO Readiness Baseline.

Assessment methodologyHow evidence is inventoried, mapped, accepted, bounded, and preserved.Trust CenterCurrent implementation properties, procurement materials, and planned maturity.Package VerifierIndependent comparison of an illustrative package manifest and artifact identities.