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Stop the agent, preserve the current state and logs, determine the exact time and scope of the change, then restore to a separate target before touching production. Whether recovery is possible depends on your database engine, backup retention, recovery point objective, and whether the agent deleted records, dropped a resource, or also removed the backups. There is no universal AI-agent-specific restore command; the reliable approach is standard database incident response applied to an automated principal with database access.
What to do in the first minutes
Treat an unexpected agent write as a live database incident. Every additional write can overwrite evidence or make a later rollback discard valid work.
- Stop further changes. Disable the agent, revoke its database token, pause the job or queue that invokes it, and place the affected application in a maintenance or read-only mode when practical.
- Preserve evidence. Record the current database state, transaction and audit logs, agent prompts or tool calls, deployment version, and timestamps before a restore or cleanup operation. Export logs to storage the agent cannot modify.
- Identify the principal. Determine which database user, service account, role, API key, or connection pool the agent used. Map its activity to the affected database, schema, tables, rows, containers, or objects.
- Bound the time window. Find the last known-good point and the first destructive statement or API call. Include delayed jobs, replicas, caches, and downstream systems in the timeline.
- Contain access. Rotate the affected credentials, revoke active sessions, restrict network access, and remove
DROP,DELETE, and broad write privileges while you investigate. AWS GuardDuty’s remediation guidance for potentially compromised RDS activity says: “Immediately change the password of the affected database user and revoke active sessions.” Apply equivalent controls on your platform. - Check recovery material. Inventory automated backups, snapshots, point-in-time logs, long-term-retention copies, replicas, exports, and cross-region copies. Confirm that each one predates the destructive action and is actually readable.
- Choose a recovery target. Prefer a new instance, database, or isolated project first. Do not restore in place until the recovered state has been inspected and the team agrees which later writes may be lost.
If the event may be an account compromise rather than a simple prompt error, involve your security team. The database response and the identity response must proceed together.
Establish exactly what changed
Record-level deletion or overwrite
A record-level incident leaves the database resource intact but changes rows, documents, blobs, indexes, or relationships. Query audit logs, transaction history, change-data-capture streams, application logs, and replica lag history to identify affected keys and the before-and-after values. A selective repair is often safer than rolling the entire database back, especially when legitimate orders, payments, or user edits occurred after the agent’s mistake.
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Schema or object destruction
A dropped table, schema, collection, container, or index can require a broader restore. Confirm whether the object can be recreated from migrations and whether its data exists in a backup. Recreating an empty table is not data recovery.
Service-resource deletion
Deleting an entire managed database, instance, project, or account is different from deleting rows inside it. Provider recovery windows, soft-delete settings, and geo-recovery options may apply to the resource, while record-level point-in-time recovery may not. Check both categories separately.
Backup or snapshot deletion
An agent or compromised credential may have removed snapshots, disabled retention, or deleted a backup vault. Search for copies outside the agent’s account: immutable object storage, cross-account backups, offline exports, replicas, and provider-managed retention. If no independent copy contains a point before the loss, a complete restoration may not be possible.
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Choose the recovery method
The decision is governed by the recovery point, the restore target, and the amount of valid work created after the incident. The options below are patterns; exact availability depends on the database service and its configuration.
| Method | Best fit | What happens | Main risk |
|---|---|---|---|
| Whole-database point-in-time rollback | Widespread corruption with little valuable work after the chosen point | The database is reconstructed as it existed at a selected time | Valid writes after that time are discarded |
| Side-by-side restore | Any incident where the production state must remain available for comparison | A new database or instance is created from a backup or log sequence | Requires temporary capacity and a controlled cutover |
| Selective extraction | Only certain rows or objects are wrong | Compare a restored copy with production and apply a recovery script or controlled data load | Relationships, identity values, triggers, and concurrent updates can be mishandled |
| Deleted-resource or geo-restore | The service resource is gone or the primary region is unavailable | Use the provider’s deleted-database, long-term-retention, or cross-region recovery path | Different target-region and naming constraints may apply |
| Redeployment from source | The agent’s executable configuration is gone but source code and configuration remain | Deploy a new agent or service from version-controlled artifacts | Capability returns, but deleted historical data or conversation state may not |
How major managed platforms behave
Amazon RDS
Amazon RDS point-in-time recovery creates a new DB instance and does not modify the source instance. Use the latest point before the first destructive statement, then inspect the new instance. AWS guidance for confirmed data loss is to use point-in-time recovery from automated backups or restore from the most recent snapshot. The exact recovery window is limited by the instance’s automated-backup retention and available transaction logs.
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After validation, you can either redirect the application to the recovered instance, copy selected data back, or use it as evidence for a more targeted repair. Keep the original instance isolated until the incident record, log retention, and rollback plan are complete.
Azure SQL Database
Azure SQL Database documents several distinct paths: point-in-time restore, restore of a deleted database, long-term-retention restore, and geo-restore. Availability and target restrictions differ by service tier, region, retention configuration, and restore mode. A restore cannot overwrite an existing database, so plan a new database name or server target and then perform a controlled cutover or data merge.
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Google Cloud Backup and DR
Google Cloud’s restore overview warns that restoring production to a selected point replaces the current application state. Its documentation states: “This restoration results in the loss of all current application data as the application will be restored to its status at the point-in-time when the backup was created.” Treat that as a destructive operation: freeze writes, identify changes after the backup, and prefer a new recovery resource or a workload-specific selective-recovery option when available.
Some workload types allow a restore to a new resource; others support selective data recovery. Verify the target behavior for the specific protected workload rather than assuming that every backup can be mounted for row-level extraction.
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Microsoft Foundry Agent Service Standard deployments
Microsoft’s guidance for the Agent Service Standard architecture distinguishes the agent application from its Cosmos DB data. In the documented scenarios, certain deleted Cosmos DB databases or containers can be restored, with service-specific estimates such as 10+ minutes for some deleted resources and 20+ minutes for some deleted dependent resources. Those are operational estimates for that Foundry architecture, not general database-recovery times.
The same guidance says Cosmos DB point-in-time restore is not helpful for the described data-level corruption cases, and a deleted production thread has no supported recovery capability in that scenario. Redeploying a deleted production agent from source control or application code can restore the service with a new agent ID, but it does not recreate deleted historical thread data. These limitations do not apply automatically to other agent frameworks or database designs.
A safe restore-and-validate runbook
1. Define the recovery point
Choose the latest timestamp that is definitely before the first unwanted change, not merely before the alert. Account for clock skew, queued agent calls, open transactions, replication delay, and jobs that committed later than their start time. Write the chosen timestamp and the evidence supporting it into the incident record.
2. Provision an isolated target
Create the restored instance or database in a separate name, account, project, or network segment when the platform permits it. Restrict application credentials from writing to this target during inspection. If the provider only offers an in-place operation for a particular resource, take an additional snapshot or export first and schedule a maintenance window.
3. Restore without application traffic
Restore the backup, transaction-log chain, or deleted resource using the provider’s current procedure. Do not let the production application reconnect automatically through a shared connection string. Label the target with its recovery timestamp and source backup so operators cannot confuse it with production.
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4. Verify structural health
- Confirm that all expected schemas, tables, collections, indexes, views, stored procedures, and permissions exist.
- Run provider integrity checks and database consistency checks where supported.
- Compare row or document counts for critical entities with monitoring records and known-good reports.
- Check sequences, identity counters, foreign-key relationships, timestamps, and encryption or key-access status.
5. Compare business state
Do not declare success because the database starts. Compare representative records, totals, status transitions, and audit trails against the last known-good application output. For selective recovery, generate a difference set keyed by stable identifiers and have the owner of each data domain approve the rows to copy.
6. Account for intervening writes
List every legitimate transaction committed after the chosen point. A full rollback will remove those changes; a selective merge must preserve them. Coordinate writes from the application, analytics pipelines, caches, search indexes, and integrations so the restored data is not immediately overwritten by stale events.
7. Test the repair on a copy
Run the extraction or merge script against a disposable clone first. Make it idempotent, log every affected key, use explicit transactions where practical, and include a rollback or compensating script. Validate foreign keys, uniqueness constraints, derived totals, and downstream events before touching production.
8. Cut over deliberately
Announce a write freeze or maintenance window, drain workers, stop scheduled agent calls, and switch connection settings only after validation. If applying selected rows to the original, use the narrowest permissions possible and monitor errors, latency, replication, and business metrics during the change.
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Compare production with the approved recovery set, reindex or rebuild caches as required, replay only events that are proven missing, and ask application owners to verify critical workflows. Keep the original evidence and pre-change snapshot under a retention policy rather than deleting them after the cutover.
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10. Close the access path
Replace temporary credentials, remove emergency privileges, re-enable only the agent capabilities that are justified, and document the exact prompt, tool call, permission, or software defect that enabled the destructive action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a full restore is the wrong answer
A whole-database rollback is usually excessive when only a bounded set of rows was overwritten and the system has accepted valuable transactions since then. It can also create legal or accounting problems by erasing an audit trail that must remain intact. Prefer a restored comparison copy and a reviewed data-recovery script when the affected keys can be identified reliably.
Conversely, selective repair is risky when the agent changed schema, cascaded deletes across many related tables, altered encryption keys, or corrupted derived data whose original inputs are unknown. In those cases, a complete restore followed by controlled replay may be easier to validate than trying to infer every damaged dependency.
What recovery can and cannot promise
- Backups are not magic time machines. A backup must predate the loss, remain readable, and include the required logs or objects.
- Retention sets the outer boundary. Once automated backups, point-in-time logs, and long-term copies expire, that recovery point disappears unless another copy exists.
- Restore semantics differ. Some services create a new target; others replace the application’s current state or impose target-region and naming limits.
- Resource recovery is not record recovery. Reappearing database infrastructure does not guarantee that deleted rows, documents, or conversation threads are available.
- Agent redeployment is not data restoration. Recreating an agent from source can produce a functioning new identity while leaving historical state permanently missing.
- No independent copy may mean irreversible loss. If the agent deleted the only database, all snapshots, and every retained log, technical recovery may be impossible; focus then shifts to exports, replicas, downstream systems, and reconstructing data from business records.
Preventing the next agent-driven data incident
Use least privilege by operation
Give agents separate identities for read, insert, update, and administrative work. Deny DROP, unrestricted DELETE, schema changes, backup deletion, and retention-policy changes unless a human-approved workflow is performing them. Restrict the agent to approved schemas and row scopes, and require a second control for destructive operations.
Add technical guardrails
- Require soft deletes, version columns, and immutable audit records for business-critical entities.
- Use database policies, stored procedures, or an approval service that validates affected-row counts and blocks mass updates.
- Set transaction timeouts and maximum row or document limits for agent-issued operations.
- Separate production credentials from development and test environments.
- Alert on unusual
DROP,TRUNCATE, bulkDELETE, retention changes, or a sudden increase in affected rows.
Make backups independent and testable
Keep copies in a separate account or project with immutable retention where supported. Maintain cross-region or offline exports for the most important data. Test both full restoration and selective extraction on a schedule; a backup that has never been restored is an assumption, not a recovery plan. Record the recovery point objective, recovery time objective, retention period, owner, and escalation path for each database.
Design the agent for reversibility
Have the agent submit a proposed change set before execution, show the number of affected records, and require approval for destructive actions. Store prompts, tool arguments, generated SQL, execution results, and correlation IDs in tamper-resistant logs. Give operators a kill switch that stops queued and in-flight work, and rehearse the procedure with a non-production database.
Bottom line
Restoration after an AI agent damages data is a database incident, not a special one-click AI feature. Stop writes, secure the identity, establish the timeline, and restore to an isolated target before deciding between a full rollback and selective extraction. AWS RDS, Azure SQL, Google Cloud Backup and DR, and agent-specific architectures expose different restore targets and limitations. The only dependable answer to “can I get the data back?” is the verified backup, log, replica, or export that contains the needed state before the destructive change.
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