Evidence the run may read
- Approved logging requirements
- System event and retention documentation
- Sample exported evidence
production blueprint · compliance & audit
AI system logging review agent blueprint.
Compare an AI system's documented event capture and retention evidence with an approved control requirement.
Search intent: AI agent logging compliance review
Published by a2a cloudProduct-source methodology →
01 · the contract
Start from a bounded job, not a blank chat box.
trigger
An AI system reaches a logging-control assessment checkpoint.
owner
compliance owner
agent stops at
The compliance and system owners approve the assessment and remediation plan.
KPI · decrease
AI logging control gaps (gaps). Count reviewed mandatory logging requirements without accepted evidence.
outputs
Artifacts the run must produce
- Requirement coverage matrix
- Evidence and retention gaps
- Control owner action plan
02 · topology
Small specialists. Named handoffs. One accountable decision.
Each stage produces an artifact another stage can inspect. The final node is a person, not an autonomous write to an external system.
- 01
intake
Validate and normalize the ai system logging review inputs.
- 02
requirement interpreter
Produce the requirement coverage matrix.
- 03
logging evidence analyst
Produce the evidence and retention gaps.
- 04
AI governance reviewer
Challenge the ai system logging review result and prepare an approval packet.
- 05
compliance owner
The compliance and system owners approve the assessment and remediation plan.
intake→ validated input packet →requirement-interpreter
requirement-interpreter→ Requirement coverage matrix →logging-evidence-analyst
logging-evidence-analyst→ Evidence and retention gaps →ai-governance-reviewer
ai-governance-reviewer→ approval packet with evidence references →human-approver
03 · authority
Grant the run only what this case needs.
Source material is read-only. Drafts land in a case-specific output path. Tools may read or propose; the human gate owns the external write.
case inputs
workspace/compliance/ai-system-logging-review/inputs/**Read only the evidence attached to this workflow instance.
case outputs
workspace/compliance/ai-system-logging-review/outputs/**Write drafts and evidence artifacts without modifying source records.
approved tools
compliance:ai-system-logging-review:read-or-proposeInvoke only tools explicitly granted for this run; external writes remain gated.
04 · implementation
A private, bounded starting manifest.
The blueprint starts private, caps its DAG, disables replanning, and exposes no public endpoint. Add only the tools and data adapters this workflow has approved.
a2a.yamlsafe starting point
name: compliance-ai-system-logging-review
version: 0.1.0
entrypoint: agent:BlueprintAgent
expose:
public: false
composition:
planning: deterministic_dag
max_nodes: 6
max_parallel: 1
max_replans: 005 · acceptance test
Pass only with evidence.
- 01
Evidence traceability
Every material conclusion cites an input artifact or a scoped tool result from this run.
- 02
Coverage claims are limited to inspected event fields, sample evidence, retention, and access controls.
Coverage claims are limited to inspected event fields, sample evidence, retention, and access controls.
- 03
Approval boundary
The run stops at a proposal and records the human decision before any external side effect.
failure containment
Stop small. Preserve the evidence.
System documentation and exported evidence do not describe the same deployed version.
Containment: Return a partial result with unresolved items; do not broaden scope or perform an external write.
Operator: Attach the missing evidence, narrow the brief, or explicitly approve a new scoped run.
A required input or tool grant is unavailable.
Containment: Stop the affected branch and preserve completed artifacts in the case output workspace.
Operator: Grant only the missing resource or continue with that branch marked out of scope.
06 · proof
Sign the run facts. Keep money in the billing ledger.
Current platform receipts sign caller identity or classification, skill, bounded input evidence, verified grant IDs when present, outcome or result preview, and timing. Optional file, tool, artifact, handoff, evaluation, and review fields require separate instrumentation and are not populated by default. Price, fees, payouts, and later human approvals remain separate platform records.
The example uses only fields populated by the current platform sealing paths. It is illustrative, not a record of a real customer run.
ExecutionReceipt · selected fieldsEd25519 token
{
"receipt_id": "rcpt_01J...",
"schema_version": 1,
"agent_name": "compliance-ai-system-logging-review",
"caller": "user:workflow-owner",
"task_id": "case_ai_system_logging_review",
"skill_name": "ai_system_logging_review",
"input_hash": "4d7c...9a2f",
"grant_ids": [
"grt_case_inputs",
"grt_tool_propose"
],
"status": "ok",
"result_preview": "Output prepared: Requirement coverage matrix. Human decision remains separate.",
"elapsed_ms": 4218
}related blueprints
Keep the operating model consistent.
Compliance & Audit
Control evidence collection
Map a control request to approved sources, collect a bounded evidence packet, and flag missing attestations.
Compliance & Audit
Audit request response
Translate an audit request into attributes, sources, owners, and an approved response packet without over-answering scope.
Compliance & Audit
Policy-to-control mapping
Map approved policy statements to controls and evidence expectations, highlighting unsupported or orphaned requirements.
put the pattern to work
Start private. Scope the authority. Require the decision.
Deploy the workflow as a bounded internal agent, verify its outputs and the receipt fields actually emitted, then expand only the scopes your acceptance test proves it needs.