Rubrik recently introduced Agent Rewind, a solution targeting the emerging challenge of AI agent error recovery. The new offering, powered by technology from Rubrik’s acquisition of AI infrastructure startup Predibase earlier this year, provides visibility, audit trails, and rollback capabilities for actions taken by autonomous AI agents across enterprise systems.

Agent Rewind addresses a gap in current AI observability tools by connecting agent actions to their root causes and enabling systematic reversal of unwanted changes.

Rubrik claims this represents the first solution offering true “rewind” capabilities for AI agent actions, positioning the company as a pioneer in what it terms “AI resilience” – an extension of its core cyber resilience business.

The new solution targets enterprises deploying agentic AI systems that can autonomously modify data, configurations, and system states, introducing new categories of operational risk that traditional monitoring tools cannot adequately address.

Technical Details

Rubrik’s Agent Rewind integrates Predibase’s AI infrastructure capabilities with Rubrik’s existing Security Cloud platform to create what the vendor describes as continuous agent action backup.

The architecture requires AI agents to be observable and cooperative, meaning they must expose their actions through APIs or other monitoring interfaces that Agent Rewind can capture and record.

The system creates immutable snapshots of system states before agent actions occur, establishing clean recovery points. Each agent action is time-stamped, catalogued with the agent’s identity, and stored in a tamper-proof format that maintains the integrity of the audit trail.

This approach enables the system to trace specific changes back to their originating prompts and decision trees.

Context-Enriched Visibility Framework

Agent Rewind provides what Rubrik terms “context-enriched visibility” that maps agent behavior from initial prompts through planning stages to actual tool execution. The system maintains an inventory of active agents across the environment and applies risk scoring based on their access permissions and historical activity patterns.

The visibility layer captures not just what actions occurred, but the decision path that led to those actions. This includes the original user prompts, the agent’s interpretation and planning process, and the specific tools or APIs the agent accessed to execute changes.

This contextual mapping enables administrators to understand why an agent took specific actions, not just what it did.

Rollback and Recovery Mechanisms

The safe rollback functionality leverages Rubrik Security Cloud’s recovery capabilities to reverse changes across multiple data types and system components. The solution can rewind changes to files, databases, application configurations, and code repositories by restoring systems to previously captured clean states.

Rubrik claims the rollback process operates with minimal downtime and includes safeguards to prevent rogue agents from attempting to reinstate their original changes during the recovery process. The system identifies the scope of agent-induced changes and can selectively roll back specific modifications while preserving other legitimate system changes that occurred in the same timeframe.

Platform Integration and Compatibility

Agent Rewind supports integration with major enterprise AI platforms, including Salesforce Agentforce, Microsoft Copilot Studio, and Amazon Bedrock Agents. The solution also accommodates custom AI agents through API integrations and standardized monitoring interfaces.

The broad compatibility approach aligns with the reality that most enterprises are likely to deploy heterogeneous AI agent environments rather than standardizing on single platforms.

However, the effectiveness of Agent Rewind depends on the cooperating agents implementing proper observability interfaces, which may require additional development work for custom or legacy AI systems.

Impact to IT Organizations

For IT practitioners, Rubrik’s Agent Rewind addresses a significant operational gap as organizations scale AI agent deployments. The solution offers clear value in environments where AI agents have broad system access and can autonomously make changes that impact critical business processes.

The primary operational benefit lies in risk mitigation. Current AI observability tools focus on performance metrics and basic activity logging, but often provide only limited insight into the business impact of agent actions or methods for reversing problematic changes.

Agent Rewind’s audit trail capabilities enable teams to trace specific business disruptions back to their AI agent origins, reducing mean time to resolution for agent-induced incidents.

However, implementation may present several practical challenges. The solution requires AI agents to expose their decision-making processes and actions through standardized interfaces, which may necessitate modifications to existing agent implementations.

Analysis

Rubrik’s entry into AI agent recoverability is a logical extension of its core cyber resilience business, leveraging existing backup and recovery capabilities to address new AI-related risks. The company’s positioning of “AI resilience” as a natural evolution from cyber resilience creates a coherent narrative that differentiates its approach from pure-play AI monitoring vendors or even traditional observability players.

The timing is right, as enterprises are just beginning to deploy production AI agents but have limited solutions for managing the operational risks these systems introduce. By claiming first-mover status in AI agent recoverability, Rubrik establishes a potential competitive moat in an emerging market segment.

However, the differentiation depends heavily on enterprises recognizing AI agent error recovery as a distinct problem requiring specialized solutions. If major AI platform vendors integrate similar rollback capabilities into their core offerings, or if traditional backup vendors quickly develop competing solutions, Rubrik’s advantage may be temporary.

The Predibase acquisition provides technical depth in AI infrastructure. Still, the success of Agent Rewind ultimately depends on Rubrik’s ability to demonstrate clear ROI for enterprises facing real AI agent incidents.

The solution’s technical approach appears sound, though its effectiveness depends heavily on AI agents implementing proper observability interfaces and enterprises recognizing agent error recovery as a priority investment area. Early adopters in industries with high operational risk tolerance, such as financial services and healthcare, are likely to find the most immediate value.

For enterprises currently deploying or planning significant AI agent implementations, Agent Rewind offers a compelling risk mitigation approach that aligns with established IT operational practices. It’s a nice fit with Rubrik’s overall portfolio.

Competitive Outlook & Advice to IT Buyers

Rubrik claims no direct competitors currently offer equivalent rewind capabilities for AI agent actions, positioning Agent Rewind as a category-creating solution. This assessment appears accurate in the near term, as most AI observability vendors focus on performance monitoring rather than operational recovery.

However, several competitive dynamics could challenge Rubrik’s positioning…

These sections are only available to NAND Research clients and IT Advisory members. Please reach out to [email protected] to learn more.