Rubrik Launches Agent Rewind for AI Mistake Recovery Infrastructure

Enterprise deployment of autonomous AI agents faces a new bottleneck: when agents make mistakes, how do organizations undo the damage? Rubrik’s new Agent Rewind, launched August 12th following their Predibase acquisition, becomes the first platform specifically designed to trace, audit, and reverse unwanted AI agent actions.

As AI agents gain autonomy to modify databases, delete files, and change configurations, the stakes of agent errors escalate beyond traditional software bugs. IDC Research Manager Johnny Yu frames this as the emergence of “non-human error” - a fundamentally new category requiring purpose-built recovery infrastructure.

Problem: Autonomous Agents, Opaque Failures

Current AI agent platforms optimize for capabilities and deployment speed, but offer limited visibility into agent decision-making processes. When agents fail, organizations face several challenges:

  • Black box operations: Traditional observability tools show what happened, but not why agents made specific decisions
  • Cascading failures: Agent errors in interconnected systems can trigger downstream problems across multiple applications
  • Recovery complexity: Undoing agent actions requires understanding the full context of changes made across files, databases, and configurations

A recent Carnegie Mellon study found AI agents fail at office tasks nearly 70% of the time, with agents frequently becoming disoriented and choosing incorrect shortcuts. As enterprises deploy increasingly autonomous agents, the cost of these failures multiplies.

Solution: Context-Aware Recovery Architecture

Agent Rewind addresses this gap by combining Predibase’s AI infrastructure with Rubrik’s data recovery capabilities to create what the company calls “AI recoverability.” The platform offers three core capabilities:

Context-Enriched Visibility: The system maps every agent action back to its root cause, from initial prompts through planning phases to specific tool usage. This creates an audit trail showing not just what changed, but why the agent made those decisions.

Safe Rollback: Integration with Rubrik Security Cloud enables precise reversal of agent changes across multiple data types - files, databases, application configurations, and code repositories. The rollback process maintains data integrity while unwinding complex multi-step agent operations.

Broad Compatibility: The platform works with existing agent frameworks and enterprise applications, avoiding the need for organizations to rebuild their AI infrastructure to gain recovery capabilities.

Evidence of Enterprise Adoption

BioIVT, a global biospecimen solutions provider, represents the type of enterprise customer driving demand for agent recovery infrastructure. Chief Information Security Officer Chad Pallett explains the use case: “When using AI, there is a need for observability and secure rollback. Rubrik and Predibase will provide not just data safety and model speed, but also AI recoverability.”

The timing aligns with broader enterprise AI adoption patterns. Organizations are moving beyond proof-of-concept deployments toward production systems where agent failures carry real business impact. Recent incidents include agents deleting production databases and making unintended legal commitments - errors that traditional backup systems cannot adequately address.

Rubrik’s approach builds on their $100+ million Predibase acquisition, completed in 2025, which brought advanced model fine-tuning and deployment capabilities. The combination creates an integrated stack addressing both agent performance optimization and failure recovery.

Implications: Infrastructure-First AI Strategy

Agent Rewind signals a market shift toward infrastructure-first approaches to enterprise AI deployment. Rather than competing on agent capabilities alone, vendors are recognizing that enterprises need comprehensive operational tooling before deploying autonomous systems at scale.

This mirrors the evolution of traditional software deployment, where companies like Datadog and PagerDuty built substantial businesses around observability and incident management. The AI agent market appears to be following a similar pattern, with specialized infrastructure layers emerging to support production deployments.

The platform also addresses compliance requirements as regulated industries consider AI agent adoption. Financial services and healthcare organizations need audit trails and rollback capabilities to meet regulatory standards - making recovery infrastructure a prerequisite rather than a nice-to-have feature.

Looking Forward

As AI agents become more sophisticated and autonomous, the challenge shifts from building capable agents to building reliable agent ecosystems. Organizations need infrastructure that enables confident deployment of autonomous systems without fear of irreversible errors.

The next 6-12 months will likely see expansion of agent recovery capabilities beyond data rollback to include workflow reversal, contract unwinding, and automated remediation of agent-initiated processes. The market for agent observability and recovery infrastructure could emerge as a critical enablement layer for enterprise AI adoption.

Agent mistake recovery represents a necessary evolution in AI infrastructure, addressing the gap between agent capabilities and enterprise operational requirements. As organizations deploy increasingly autonomous systems, platforms like Agent Rewind provide the safety net that enables confident scaling of AI operations.

For infrastructure teams building AI agent orchestration systems, recovery capabilities complement deployment and monitoring tools to create comprehensive operational frameworks for enterprise AI adoption.