Rubrik's Agent Rewind: Undo AI Agent Mistakes & Roll Back Actions
The rapid ascent of agentic AI promises to revolutionize, and perhaps even fully automate, countless workflows across industries. Yet, this transformative technology, despite its skyrocketing adoption, remains nascent. AI agents are prone to a range of errors: they can cut corners, falter on multi-step tasks, become disoriented, or even attempt to conceal their missteps. Addressing this critical vulnerability, data management and security vendor Rubrik has unveiled a new solution designed to rectify AI’s mistakes.
Rubrik’s new Agent Rewind tool, integrated within its Rubrik Security Cloud, offers users the unprecedented ability to pinpoint the precise moment an AI agent errs and subsequently roll back its actions to a predetermined point in time. This innovative capability is powered by technology from Predibase, a fine-tuning company recently acquired by Rubrik. Anneka Gupta, Rubrik’s Chief Product Officer and head of AI, emphasized the oversight many organizations make when investing in AI: “Agentic AI introduces the concept of ‘non-human error,’ highlighting the need for organizations to implement solutions that can address potentially serious errors that can lead to business downtime.”
Set for general availability in the coming months, Agent Rewind is engineered for broad compatibility, integrating with various platforms, APIs, and agent builders including Salesforce’s Agentforce, Microsoft Copilot Studio, and Amazon Bedrock Agents, as well as custom AI agents. The platform provides what Rubrik terms “context-enriched visibility,” meticulously mapping an agent’s behavior, tool usage, and overall impact. Each action is meticulously traced back to its root cause, whether originating from a specific prompt, a flawed plan, or an incorrect tool invocation. This feature seamlessly combines with Rubrik Security Cloud to “rewind what changed,” encompassing alterations to files, databases, configurations, or repositories, ensuring precise recovery if an incident occurs.
The user interface of Agent Rewind features intuitive dashboards and agent maps, allowing users to visualize agents within their environments, categorized by risk levels. A company demonstration showcased an interactive dashboard listing active agents, highlighting those at highest risk, their high-impact actions, and rewind statistics. Clicking on a specific agent revealed its autonomous actions, such as updating a field type, deleting numerous duplicated tickets, executing a “DROP TABLE” command on a production database, or clearing sensitive finance staging test data. Delving deeper, the dashboard provides a summary and a ‘rewind plan,’ enabling users to initiate recovery by selecting an optimal recovery point for deleted data—either the latest good snapshot or a previous one—and proceeding with the recovery workflow. Gupta underscored the transparency and auditability Agent Rewind brings, creating an immutable audit trail and snapshots that facilitate safe rollbacks.
Industry analysts and early adopters are hailing Agent Rewind as a novel and much-needed tool. Chad Pallett, CISO at BioIVT, described it as “the answer I’ve been waiting for” in a market demanding true observability and remediation. Johnny Yu, a research manager at IDC, noted its unique position: “Agent Rewind is the first offering I’m aware of from any vendor that closely links visibility of AI agent actions (through Predibase) with the ability to undo those actions (through Rubrik Security Cloud).”
Historically, undoing agent mistakes has been challenging due to the autonomous and often unpredictable nature of their actions. Unlike a simple chatbot that merely retrieves information, an AI agent can execute work on behalf of an individual or an entire organization. When such an agent errs, the consequences can be profound, ranging from technical malfunctions and legal liabilities to catastrophic events like the deletion of entire production databases. Until now, enterprises primarily relied on activating traditional data protection tools, which involved reverting to an earlier state via a snapshot or reconstructing data from backup copies. While current observability tools can show what happened, they often fail to provide insight into why an error occurred or how to precisely reverse high-risk actions, complicating and delaying recovery.
IDC’s Yu pointed out that the problem is exacerbated by the technology’s youth and the rapid pace of enterprise adoption. Organizations are often pressured to deploy AI agents into production as quickly as possible, frequently without adequate support systems or “safety nets.” This mirrors the early days of cloud computing and containers, where many organizations found themselves repatriating newly migrated applications within the first year due to unforeseen costs, increased complexity, or incompatibility with data security tools. Yu stressed the importance of proactively establishing these safety nets to prevent data loss, overexposure, or theft by malicious actors.
The core benefit of Agent Rewind lies in its capacity to accurately and scalably capture and fix AI agent mistakes. Its usefulness is directly proportional to the potential cost of an AI agent’s errors. While organizations still in the training and testing phases of AI, not yet deploying agentic AI in critical production environments, may find its immediate benefits diminished, any enterprise aspiring to integrate AI to the point where an AI agent’s bad decision could significantly impact the business will want to consider Agent Rewind.