Freshworks buys Robusta to bring AI outage investigations into Freshservice

Freshworks has acquired Robusta Dev Ltd., adding AI technology designed to investigate outages before engineers have to piece together the evidence themselves. Announced October 28, 2026, the deal will bring Robusta’s capabilities into Freshservice, its IT service management platform. The purchase is complete; the integration remains work ahead.

Financial terms were not disclosed, according to Entrackr. Freshworks also plans to deepen connections with incident-response platforms such as FireHydrant, linking diagnosis with the workflows teams use to coordinate repairs.

Investigating the failure, not just the alert

Freshworks describes a familiar obstacle in outage response: responders must assemble clues from monitoring tools, alert logs, ticket histories and deployment records before they can begin fixing the underlying problem. Robusta is intended to automate that investigation and deliver findings inside the incident workflow, rather than leave engineers to reconstruct the sequence manually.

The company breaks Robusta’s operation into three stages. These span collecting evidence, explaining what failed and proposing a response—not simply summarizing an alert.

• Sense: When an anomaly appears, AI agents examine monitoring data, infrastructure measurements and recent code or configuration changes for clues to what happened.
• Reason: The engine identifies the underlying cause, maps which dependent services are affected and presents evidence supporting its conclusions, according to Freshworks.
• Act: It recommends fixes within customer-defined limits, allowing engineers to review and approve changes before they reach production—the live systems a business depends on.

Scale is part of the technical pitch. Freshworks says Robusta has been optimized for large production environments, where the number of alerts and the complexity of systems make running AI investigations difficult. Robusta co-founder and CEO Natan Yellin identifies the cost of processing those events as a constraint.

Our technology lets AI Agents do that effectively in large enterprise environments, where there are so many raw events that investigating each one individually would be exorbitant from a token cost perspective.

Natan Yellin, Robusta co-founder and CEO

Diagnosis has to reach the response team

The planned combination gives the investigation more company-specific context. Freshworks says Robusta investigations will draw on Freshservice’s business-service information, records of dependencies between systems and histories of changes. That is intended to help trace a disruption through the customer’s actual environment, rather than offer a generic explanation of what might have gone wrong.

The next handoff is to the people managing the incident. Freshworks describes diagnostic evidence, affected services and suggested repair steps flowing into FireHydrant incident timelines and Slack or Teams command channels. It also plans to feed AI-generated analyses, pre-filled timelines and suggested runbooks—step-by-step response instructions—into FireHydrant and Freshservice.

Freshworks expects the integration to consolidate hundreds of alerts from a failing service into one investigated incident, reducing duplicate triage. It also aims to shorten the time needed to assemble responders and resolve incidents. These are company-stated goals, not measured improvements supplied with the announcement.

Using the last outage to prevent the next

Freshworks also wants incident findings to change how future work is checked. Its plan calls for post-incident data to feed FireHydrant retrospectives and update Freshservice’s controls for approving changes. The intended result is a feedback loop: evidence gathered during one disruption informs the safeguards applied before another change goes live.

The prevention work includes validating changes after deployment and turning recognized incident patterns into automated checks before later deployments. Freshworks presents that as a way to preserve operational knowledge that might otherwise remain scattered after an incident review. The acquisition’s ambition therefore extends beyond producing a faster diagnosis: it is to carry that diagnosis into subsequent operating decisions.