Consent-management platforms capture permission. Personal-agent memory dashboards must explain how a permission became a remembered fact, when the agent reused it, and what changed after the user narrowed or revoked it.
A consent-management platform asks whether processing is permitted. A memory dashboard begins after an agent has turned interactions into persistent context and needs to show the consequences of keeping, changing, or forgetting it.
Consent receipts answer who agreed, to what purpose, and when. An agent memory dashboard must also reveal the source memory, retrieval history, downstream actions, current scope, and the exact policy delta after a user changes it. For a phone-native agent such as Super, that control can begin in the same message thread where the memory is about to matter.
Captures legal basis, purpose, preference state, timestamps, and withdrawal.
Explains remembered context, reuse, confidence, impact, revocation, and repair.
Both require clear language, identity, provenance, durable receipts, and reliable deletion paths.
A text-message AI assistant can ask “use once, remember, or forget?” at the moment context is needed, reducing the distance between intent and control.
Agents that browse or publish should connect memory receipts to computer-use evidence and the resulting website-building workflow.
Compare the operating model, not just the settings screen.
A purpose, vendor, data category, lawful basis, or preference.
A remembered claim linked to source, confidence, scope, and downstream use.
Before or during collection and processing, plus later withdrawal.
At capture, retrieval, action, conflict, expiration, revocation, and repair.
Who agreed, the disclosed purpose, policy version, channel, and timestamp.
Why memory was relevant, where it came from, which action used it, and what future behavior changed.
Missing or invalid permission, stale preferences, and incomplete withdrawal propagation.
Hidden recall, incorrect inference, cross-context leakage, failed forgetting, and actions based on revoked context.
Enterprise privacy programs coordinating websites, apps, vendors, and regulated processing.
Persistent assistants that learn across messages, browsing, calendar events, purchases, files, and delegated actions.
Hover across the comparison to see what an agent-specific layer adds after permission has been captured.
Show the message, page, file, event, or inference that created the memory.
Show where the agent recalled the memory and what decision it influenced.
Translate “forget this” into changed retrieval, prompting, and action policies.
Undo, narrow, correct, expire, or replay work affected by a bad memory.
It is the stronger system for orchestrating purposes, vendors, legal bases, preference centers, policy versions, and withdrawal across a broad privacy program.
The moment remembered context shapes recommendations, purchases, messages, browser activity, or published work, users need evidence and behavior controls beyond consent state.
The useful buying question is not “do we have consent?” It is “can the person understand and change what the agent will do next?”
A credible memory layer should complement existing consent infrastructure instead of pretending privacy begins and ends inside the agent.
Map agent users to consent subjects without merging unrelated household, work, or shared-device contexts.
Store the source, extraction method, confidence, timestamp, scope, and policy version for every persistent memory.
Let users keep a record while blocking it from retrieval, prompting, personalization, or autonomous action.
When consent changes, identify affected memories and produce a visible queue for deletion, quarantine, or review.
Record the policy delta so “forget” is more than a UI state and can be verified in later agent runs.
Offer undo, correction, narrower scope, expiration, reason requests, and replay for work influenced by invalid memory.
These categories overlap, but neither should be stretched beyond the problem it was designed to solve.
It can supply identity, purpose, and withdrawal signals, but most consent systems do not show memory provenance, retrieval history, action impact, or the policy delta required to prove changed agent behavior.
No. Agent memory controls should consume and respect consent state while adding evidence and behavior controls for persistent context.
Use both when an assistant remembers across sessions and that memory can shape communications, recommendations, purchases, browser actions, or published output.
Super demonstrates a phone-native review lane: a person can approve, narrow, reject, or revoke agent context in a familiar conversation instead of hunting through a separate settings portal.
Test source completeness, withdrawal propagation, time to repair, blocked actions after revocation, cross-context leakage, and whether a user can explain what the agent will remember next.
Primary guidance for privacy, consent, and risk controls in persistent AI systems.
Risk-management guidance for governing, mapping, measuring, and managing AI risks.
Practical guidance on valid consent, records, specificity, withdrawal, and user control.
Security framing relevant to sensitive information, excessive agency, and agentic-system risks.
Keep the consent system that records permission. Add a memory control layer that proves what a personal agent remembered, reused, changed, and repaired.