Vendors Building Tools to Clean Up AI Agent Messes as Agentic AI Adoption Accelerates

The rapid adoption of agentic AI in enterprise environments has created an unexpected challenge: what happens when AI agents make mistakes? Three major vendors—Cohesity, ServiceNow, and Datadog—have partnered to address this growing concern by developing tools specifically designed to clean up the messes created by AI agents operating enterprise systems.

The AIOps Recovery Challenge

The partnership centers on a recoverability service that will hunt down files and data corrupted by errant AI agents and restore systems to a "trusted state." This initiative acknowledges a fundamental reality of AI adoption: while agentic AI promises to automate complex operations (a practice often called "AIOps"), these systems can and do make mistakes—sometimes catastrophic ones.

The vendors expect enterprises will increasingly deploy agentic AI to manage critical infrastructure, but recognize that these AI systems could either botch their assigned tasks or fall victim to malicious attacks. The result is a market need for tools that can quickly recover from errors introduced by AI automation.

Market Context and Competition

Cohesity plans to deliver its recoverability product before the end of 2026, entering a market that's already showing signs of maturity. Rubrik introduced a similar tool in August 2025, and established players like Cisco have built native rollback capabilities into their agentic tools.

The market opportunity appears substantial. Gartner predicts that up to 40 percent of enterprise applications will include integrated task-specific agents by 2026, representing a dramatic increase from less than five percent in 2025. This explosive growth suggests that AI agent-related errors could become a significant operational challenge for IT departments worldwide.

Technical Approach to AI Recovery

Cohesity's solution addresses the risks posed by AI agents through several key mechanisms:

• Immutable snapshots: The system preserves immutable snapshots of AI environments, creating reliable recovery points
• Point-in-time recovery: Organizations can recover agents, data, and supporting infrastructure to specific moments before errors occurred
• Comprehensive coverage: The tool can restore files, databases, object storage, SaaS applications, vector stores, and agent memory

ServiceNow and Datadog contribute critical capabilities to the partnership. ServiceNow provides control and observability platforms that monitor for anomalies, while Datadog offers monitoring and alerting capabilities. When these tools detect problems, Cohesity's system can trigger API-driven restorations across an entire IT estate.

What Gets Recovered

The recoverability suite is designed to handle the full spectrum of AI-related data and configurations:

• AI agents and their operational memory
• Vector databases that store semantic information
• Model configurations and parameters
• Training and fine-tuning data
• Enterprise data stores affected by AI operations

This comprehensive approach ensures that organizations can restore not just their data, but the entire AI operational environment to a known good state.

Industry Warnings and Best Practices

Forrester has warned that preventing agentic AI problems requires developers to include guardrails, identity and access management controls, and strong oversight from the outset. The fact that major vendors are building recovery tools suggests that these preventative measures may not be universally implemented or may prove insufficient as AI systems become more autonomous.

The emergence of this market segment reflects a maturing understanding of AI risks in enterprise environments. Rather than assuming AI systems will operate flawlessly, organizations are preparing for the inevitable errors and developing the tools to quickly recover when things go wrong.

Market Implications

The development of AI recovery tools represents a significant evolution in enterprise AI strategy. Instead of treating AI as a perfect automation solution, organizations are acknowledging the need for safety nets and rollback capabilities. This pragmatic approach may accelerate AI adoption by reducing the perceived risk of deployment.

For IT professionals, these tools offer a safety valve for AI experimentation. Organizations can deploy agentic AI with greater confidence, knowing they have mechanisms to quickly undo mistakes. This could lead to more aggressive AI adoption strategies, as the cost of failure decreases.

Looking Ahead

As agentic AI becomes more prevalent in enterprise environments, the market for recovery and rollback tools is likely to expand. The partnership between Cohesity, ServiceNow, and Datadog represents just the beginning of what could become a significant category of enterprise software.

Organizations deploying AI agents should consider not just the automation capabilities they're implementing, but also the recovery mechanisms available to them. The ability to quickly roll back AI-induced errors may become as important as the AI capabilities themselves in determining successful AI deployments.

The race to provide AI recovery solutions is on, and with Gartner's predictions of widespread AI agent adoption, the market for these tools could grow rapidly in the coming years. Companies that master both AI deployment and AI recovery may have a significant competitive advantage in the emerging agentic AI landscape.