Task audit logs explain completed work.
They are best for answering: what did the agent do, when did it happen, what tool ran, and what output was produced? They are essential for debugging and accountability.
Consent ledgers vs task audit logs.
Task audit logs show what a personal AI agent did. Consent ledgers show how the agent's permission boundary changed before it acts next. Operators need both once agents learn from corrections.
Different questions Audit logs ask what happened. Consent ledgers ask what future authority changed.
Competitor comparison
Audit logs are retrospective. Consent ledgers are prospective.
Audit logs are necessary for personal AI agents, but they are not enough. A task audit log records the action, timestamp, tool, and result. A consent ledger records the learned permission change that affects future behavior. If an operator corrects a draft, narrows a browser action, or changes when an agent should ask, the system needs a ledger entry, not just a task record.
Action record
Audit logs capture events after the agent acts.
Boundary record
Consent ledgers capture permission changes before future action.
Review path
Ledgers need approve, narrow, reject, expire, and rollback controls.
Super as approval surface
Super can route high-consequence consent changes through text. The text message AI assistant use case fits approvals that cannot wait for a dashboard session.
Proof for browser actions
When a consent change comes from browser work, attach evidence. The computer-use cache and AI website-building workflow show how replayable proof supports trust.
Operating model
Use logs for actions and ledgers for learned authority.
The clean pattern is not either-or. Keep audit logs for task execution. Add consent ledgers where corrections alter what the agent is allowed to do next.
Log every meaningful task.
Record tool call, result, timestamp, inputs, outputs, and failure state so completed work remains inspectable.
Create ledgers for permission changes.
When a correction changes approval thresholds, privacy handling, external communication, or browser-action scope, write a consent entry.
Route ledger review by consequence.
Low-risk entries can batch. Sensitive entries should interrupt by text with approve, narrow, or reject actions.
Link logs and ledgers.
The best receipt connects the action that caused the correction with the permission update that follows.
Layer choice
Choose the right record for the question.
Audit log
What happened during the task?
Consent ledger
What future permission changed?
Approval route
Who should review the change?
Receipt
How do we repair it later?
Checklist
Consent ledger entry checklist.
Source task Link to the audit log that caused the change.
Old boundary State what the agent believed before.
New boundary State the proposed future behavior.
Scope Limit by person, project, channel, task, or date.
Reviewer Capture who approved or narrowed the change.
Recovery Include expiration and rollback instructions.
FAQ
Common comparison questions.
Can a task audit log become a consent ledger?
It can trigger one, but it should not replace one. The ledger needs a structured before-and-after permission record.
Should every task create a ledger entry?
No. Only tasks that create or reveal a change in future approval boundaries need consent ledger entries.
What should happen when the two disagree?
The audit log is evidence. The consent ledger is policy memory. If a ledger entry overreaches the evidence, narrow or reject it.
Record actions. Govern changes.
Personal AI agents need task audit logs for work they completed and consent ledgers for authority they learned. The combination is what makes autonomy reviewable.