a2a cloud
9 governed workflows

AI agent blueprints for Compliance & Audit.

Control workflows that preserve source references and human attestation while organizing evidence and exceptions. Each pattern defines the evidence it reads, the artifacts it produces, the authority it may exercise, and the human who makes the consequential decision.

built for

Compliance, internal audit, risk, and control-owner teams

approval owner

compliance owner

default posture

private · read/propose · human-gated

workflow index

Nine jobs with a crisp acceptance test.

These are implementation patterns, not generic “AI for Compliance & Audit” pages. Open one to see its exact trigger, topology, grants, approval boundary, failure modes, and KPI.

015 nodes

Control evidence collection

Map a control request to approved sources, collect a bounded evidence packet, and flag missing attestations.

measure

evidence requests accepted first pass

Open blueprint →
025 nodes

Audit request response

Translate an audit request into attributes, sources, owners, and an approved response packet without over-answering scope.

measure

audit response turnaround

Open blueprint →
035 nodes

Policy-to-control mapping

Map approved policy statements to controls and evidence expectations, highlighting unsupported or orphaned requirements.

measure

unmapped policy requirements

Open blueprint →
045 nodes

Access control sample preparation

Select or ingest an approved sample and assemble entitlement, approval, and change evidence for control testing.

measure

sample evidence preparation time

Open blueprint →
055 nodes

Vendor compliance evidence follow-up

Track required vendor artifacts, expiry, gaps, and approved follow-ups without asserting control coverage prematurely.

measure

overdue vendor evidence

Open blueprint →
065 nodes

Compliance exception register review

Review exception expiry, ownership, compensating evidence, and remediation status and prepare an escalation queue.

measure

past-due compliance exceptions

Open blueprint →
075 nodes

AI system logging review

Compare an AI system's documented event capture and retention evidence with an approved control requirement.

measure

AI logging control gaps

Open blueprint →
085 nodes

Control remediation validation

Compare a control finding and acceptance criteria with current evidence and prepare an independent validation result.

measure

remediations reopened

Open blueprint →
095 nodes

Regulatory change impact review

Structure an approved regulatory update into changed obligations, affected controls, owners, and counsel questions.

measure

unowned regulatory actions

Open blueprint →
shared operating model

The workflow is autonomous. The authority is not.

  1. 01

    Attach evidence

    Put only this case’s approved inputs in a scoped workspace.

  2. 02

    Run specialists

    Each bounded node produces an artifact the next node can challenge.

  3. 03

    Stop at proposal

    External writes stay outside the agent’s default authority.

  4. 04

    Record the decision

    Keep the human approval as a decision record beside the signed evidence from the agent run.

Pick one expensive, inspectable job.

Start with real evidence, a named decision owner, and a measurable result. Expand the agent only after the receipt and acceptance test prove the workflow holds.