Stop the agent’s database access first, preserve logs and the current state, then identify a recovery point that predates the destructive change. The safest default is to restore into a separate database or instance, validate it, and either extract only the lost records or deliberately roll the application back. Restoring directly over production can destroy valid work created after the incident.
There is no universal AI-agent-specific restore command. The correct procedure depends on the database engine, backup retention, point-in-time recovery settings, region, permissions, and whether the agent deleted records, overwrote values, dropped objects, or deleted the database resource itself.
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What to do in the first minutes
Treat an AI agent that changed production data as a database incident, not as an ordinary undo operation. Every additional write can overwrite evidence or make a later recovery point less useful.
- Stop further writes. Disable the agent, pause its job queue, remove its database connection, or place the application in a maintenance mode that prevents writes. Leave read-only access available only to the people investigating.
- Preserve the current state and evidence. Where operationally feasible, capture a snapshot or backup of the current database before restoring anything. Export relevant activity, audit, authentication, and application logs. Record the incident time in UTC and preserve the agent’s prompts, tool calls, generated SQL, and execution results.
- Identify the principal and scope. Determine which database, account, service identity, or connection pool the agent used. Establish the first and last suspicious timestamps, then identify affected tables, rows, schemas, containers, or dropped objects from database activity logs. AWS GuardDuty’s remediation guidance for destructive RDS activity recommends this investigation.
- Contain access. Rotate the affected database password, revoke active sessions, restrict network access, and remove
DROPandDELETEprivileges while the incident is investigated. AWS states: “Immediately change the password of the affected database user and revoke active sessions.” Apply equivalent controls for the database platform you actually use. - Stop automated recovery jobs that could overwrite evidence. Do not let a replication, import, cleanup task, or agent retry continue changing the affected data until its behavior is understood.
- Record decisions. Write down the selected recovery point, the expected loss of post-incident transactions, who approved the restore, and whether the plan is full rollback or selective extraction.
Do not start by restoring in place. A restore can replace valid production changes and, on some services, cannot be undone.
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Establish exactly what was lost
Record-level deletion or overwrite
The database still exists, but rows or field values changed. Use activity logs and application records to determine which objects were touched and when. A point-in-time copy before the first bad transaction is usually the reference state. If only a small set of records is affected, restoring a separate copy and extracting those records avoids rolling back unrelated work.
Schema or object deletion
A dropped table, schema, container, index, or other object may require a backup containing the object definition and its data. Confirm that the chosen recovery point predates the drop and that dependencies such as foreign keys, views, permissions, and application migrations are included.
Database-resource deletion
Deleting the managed database resource is different from deleting rows inside it. Recovery options depend on the provider’s deleted-resource retention, snapshots, long-term retention, or geo-recovery configuration. A service may be able to recreate the database resource while still lacking a way to recover particular records or conversation threads.
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Agent deletion versus data deletion
Recreating an agent does not necessarily restore its historical data. Microsoft Foundry’s Agent Service Standard guidance describes redeploying a deleted production agent from source control or application code; the redeployment produces a new agent ID and restores capability, but it does not automatically restore deleted historical thread data.
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Choose a recovery point without losing valid work
List every available automated-backup point, snapshot, long-term-retention copy, and cross-region copy. The useful point is the latest one that is demonstrably before the destructive change and still contains the required objects.
| Decision | What to verify | Main risk |
|---|---|---|
| Point-in-time recovery | The service was retaining logs and the target timestamp is before the first bad write. | Transactions after the selected time may be absent from the restored copy. |
| Snapshot restore | The snapshot contains the affected database, schema, and records. | A snapshot may be older than the incident and require more reconciliation. |
| Deleted-resource recovery | The provider still retains the deleted resource and permits the required restore mode. | Resource recovery may not restore data-level corruption or deleted application state. |
| Selective extraction | A restored copy can be queried and compared with current production. | Applying records can violate newer business changes or relationships if not reviewed. |
| Full rollback | Stakeholders accept losing all valid writes after the chosen point. | Current application data is replaced by the older state. |
Google Cloud Backup and DR warns that a restore can revert production data to the selected point and lose data created afterward. 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 change-control decision, not a routine button click.
The safest restore workflow
- Freeze writes and preserve evidence. Keep the affected source unchanged long enough to capture the state, logs, and incident timeline.
- Inventory recovery points. Note each point’s timestamp, region, retention status, database version, and whether it is a full backup, snapshot, or point-in-time log chain.
- Restore to an isolated target. Prefer a new instance, server, database, or resource. Use a separate network path and credentials so the agent cannot connect to the copy. Amazon RDS point-in-time recovery creates a new DB instance without modifying the source. Azure SQL restore also cannot overwrite an existing database.
- Validate the restored copy. Check that required tables, rows, schemas, permissions, indexes, and application metadata exist. Compare counts and checksums where your platform supports them. Test representative records and the queries that failed in production.
- Reconstruct the incident window. Compare the restored copy with the preserved production snapshot. Identify records changed after the recovery point, including legitimate writes that must not be discarded.
- Select a recovery method. Use selective extraction when only part of the data is wrong and a full rollback when the affected state is broad and the business accepts losing later writes.
- Coordinate dependent systems. Pause consumers, caches, search indexes, reports, queues, and integrations that could reintroduce bad data or assume the newer state. Reconcile transactions that were in flight when writes were stopped.
- Apply and verify the change. For selective recovery, use a reviewed data-recovery script or controlled migration against the original database. Run it with the least privilege needed, capture affected-row counts, and verify foreign-key and business-rule consistency.
- Cut over deliberately. Change the application connection only after validation and approval. Keep the original source and restored copy available for forensic comparison until the incident is closed and retention requirements are satisfied.
- Re-enable the agent last. Give it a new, narrowly scoped identity, test it against non-production data, and require approval for destructive statements before restoring production access.
How major managed platforms behave
| Platform or service | Documented recovery behavior | Important limitation |
|---|---|---|
| Amazon RDS | Point-in-time recovery creates a new DB instance and leaves the source instance unmodified. | You still need a retained automated-backup log chain or snapshot covering a time before the incident. |
| Azure SQL Database | Automated recovery includes point-in-time restore, restore of a deleted database, long-term-retention restore, and geo-restore, subject to service and configuration constraints. | A restore cannot overwrite an existing database; target and region rules differ by restore mode. |
| Google Cloud Backup and DR | Depending on workload type, a restore can target a new resource or support selective data recovery. | Restoring production to an earlier point replaces current application data and can discard later writes; the operation cannot be undone. |
| Microsoft Foundry Agent Service Standard architecture | Some deleted Cosmos DB databases or containers can be restored, and a deleted agent can be redeployed from source code. | The documented Cosmos DB point-in-time capability is not helpful for the described data-level corruption cases, and a deleted production thread has no supported recovery in that scenario. Redeployment creates a new agent ID. |
These behaviors are platform-specific. They are not a universal guarantee for every database engine or every agent architecture.
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Selective recovery is usually the least destructive option when an agent changed a subset of records. Microsoft’s Azure SQL guidance for recovering from a user or application error is to restore a database, write and run a data-recovery script against that restored copy, and apply the recovered data to the original database.
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- Restore the candidate point to a separate target.
- Identify the damaged rows by primary key, timestamp, audit record, or application log.
- Compare restored and current values, including related parent and child records.
- Prepare a reviewed script that inserts, updates, or deletes only the approved records. Keep the script in version control and make it rerunnable or safely transactional.
- Run the script during a controlled write pause, using a service identity that cannot alter unrelated schemas.
- Record the number of rows selected, changed, skipped, and rejected. Investigate every mismatch rather than forcing a blind overwrite.
- Resume dependent services in an order that prevents stale queues or caches from reintroducing the damaged values.
Do not copy an entire table merely because one column was overwritten. A full-table replacement can erase valid edits made after the recovery point and can break records created since that point.
When a full rollback is the right choice
Rollback is appropriate when the destructive activity affected a large, clearly bounded interval and the organization accepts losing all valid changes made afterward. Obtain explicit approval from the data owner and application owner. Before the cutover:
- Export or snapshot the current production state so post-incident writes remain available for reconciliation.
- List every application and integration that writes to the database.
- Stop those writers and drain or quarantine their queues.
- Restore and validate the target at the selected time.
- Reconcile legitimate post-point transactions from logs or application records.
- Switch connections, monitor errors, and keep a rollback plan for the cutover itself.
Never describe a full rollback as an undo button. Once production is replaced, recovering the discarded newer state may require the snapshot you created immediately before the change.
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Do not assume that scenario has one universal answer. First distinguish a deleted database resource from deleted backup objects, expired retention, and records corrupted inside an intact resource. Check all configured retention tiers, immutable or cross-region copies, long-term-retention stores, and copies controlled by a separate account or project. The exact options depend on the provider and configuration.
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If no retained recovery point contains the missing data, a platform restore cannot recreate it. You may still have application exports, audit records, downstream replicas, event logs, or user-submitted copies, but their completeness and consistency must be assessed rather than assumed. If backups and independent records are genuinely absent, communicate that the missing values may be unrecoverable and focus on preserving remaining evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Recognize the limits of point-in-time recovery
Retention limits
Point-in-time recovery works only inside the configured retention window and only while the required backup and transaction-log chain is intact. A snapshot taken after the agent’s change is not a pre-incident recovery point.
Replacement is not repair
Some services restore by replacing the target’s data with the selected historical state. That can remove legitimate current work. Other services create a new resource, which is safer for investigation but still requires application cutover and reconciliation.
Data-level corruption can outlive resource recovery
Microsoft’s Foundry guidance specifically notes that Cosmos DB point-in-time restore is not helpful for the described data-level corruption scenarios. A database or container may be recoverable while the particular corrupted records or deleted thread state are not supported by that service’s recovery path.
Capability recovery is not history recovery
Redeploying an agent from source control can restore the software and configuration needed to operate, but it does not imply that deleted conversations, threads, or database rows return with the new deployment.
Verification checklist before reopening production
- The destructive principal is disabled, rotated, or restricted.
- The selected recovery timestamp is documented and demonstrably before the first bad write.
- The restored copy contains the expected schema, permissions, and data.
- Records created after the recovery point have been preserved or intentionally rejected.
- Foreign keys, unique constraints, triggers, queues, caches, search indexes, and reports are consistent.
- Application smoke tests pass with production-like traffic in an isolated environment.
- Monitoring is watching error rates, latency, write volume, and unexpected destructive statements.
- Owners have approved either selective extraction or full rollback.
- The recovery script, row counts, logs, and approvals are retained with the incident record.
Common mistakes and how to correct them
| Mistake | Why it causes more damage | Safer correction |
|---|---|---|
| Letting the agent retry | A retry can continue destructive writes while evidence is being collected. | Disable its credentials, sessions, queues, and scheduled jobs first. |
| Restoring directly over production | Valid post-incident transactions may be destroyed and the operation may be irreversible. | Restore a separate target, compare it, then choose extraction or approved rollback. |
| Choosing the newest backup automatically | The newest copy may already include corrupted data. | Choose the latest point proven to precede the first destructive change. |
| Ignoring region or target rules | Geo-restore, point-in-time restore, and deleted-database recovery can have different constraints. | Check the provider’s current target, region, edition, and retention requirements. |
| Replacing a whole table for a few bad rows | Unrelated newer records can be lost. | Extract and apply only reviewed records from the restored copy. |
| Assuming an agent redeployment restores conversations | Code and identity can return while historical thread data remains deleted. | Recover the backing data separately and verify whether the service supports it. |
| Failing to preserve the pre-restore state | Later investigation and reconciliation become harder or impossible. | Capture a snapshot or export before any replacement operation. |
Prevent the next agent-induced database incident
Limit what the agent can do
- Use a dedicated database identity for each agent and environment.
- Grant read access by default; grant narrowly scoped write access only where required.
- Separate migration, maintenance, and destructive privileges from ordinary application permissions.
- Require human approval or a separate privileged workflow for
DROP, bulkDELETE, schema changes, and updates without a restrictive predicate. - Restrict network paths so the agent can reach only the intended database and environment.
Make recovery independent of the agent
- Enable automated backups and point-in-time recovery where the platform supports them.
- Set retention to cover the time your team needs to detect and investigate an incident.
- Maintain snapshots or copies in a separate account, project, region, or administrative boundary where appropriate.
- Test restoration into a new target; a backup that has never been restored is an assumption, not a verified recovery plan.
- Keep application code, agent configuration, prompts, and deployment definitions in source control so capability can be redeployed without relying on the damaged database.
Monitor agent database behavior
Alert on destructive statements, unusual row counts, schema changes, access from unexpected networks, privilege changes, and writes outside approved tables. Retain enough database activity and application logs to identify the principal, affected objects, and time window. Logging is what turns an ambiguous “the agent changed data” report into a bounded recovery decision.
Set recovery objectives before an incident
Define a recovery point objective: how much data loss, measured in time, the business can accept. Define a recovery time objective: how long the service can remain unavailable. These choices determine backup frequency, retention, replication, staffing, and whether selective extraction is practical.
Also document who can authorize a full rollback, who owns the database, who owns the agent, and which dependent systems must be paused. Practice the complete sequence—disable access, preserve evidence, restore an isolated target, validate, extract or cut over, and reconcile—without waiting for a real data-loss event.
The reliable pattern is simple even though the implementation is platform-specific: stop the agent, establish scope and timing, select a recovery point before the destructive change, restore separately, validate against production, and apply only the recovery that the evidence and business owners justify.
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