Rootly has acquired ThinkHive, an AI agent reliability platform, as it seeks to extend reliability engineering beyond traditional software infrastructure and into large language model workloads. The transaction brings ThinkHive’s founders and team into Rootly, where they will focus on improving the dependability of AI agents used in incident response and broader production environments. The announcement did not disclose the financial terms of the acquisition.
Expanding Reliability Engineering for AI
Rootly provides an AI-native platform for on-call operations and incident management, helping engineering teams coordinate responses, identify causes, and learn from system failures. The company argues that AI agents introduce a different class of reliability risk because they can return plausible but incorrect answers while conventional dashboards continue to show healthy latency and availability. By acquiring ThinkHive, Rootly aims to detect these less visible failures before they affect customers or critical engineering workflows.
Addressing Hallucination and Model Drift
Traditional monitoring systems are designed largely for deterministic software, where outages, error rates, and performance degradation produce recognizable technical signals. AI agents can instead hallucinate, lose accuracy as conditions change, or regress after model and prompt updates without triggering established alerts. Rootly said engineering teams increasingly need tools that evaluate whether an agent completed its intended task correctly, rather than simply confirming that it generated a response.
ThinkHive’s Evaluation Technology
ThinkHive traces the individual steps taken by an AI agent and combines metrics, operational traces, evaluations, and business data to assess performance. Its platform is designed to identify hallucination and drift, group recurring problems into patterns, recommend potential corrections, and test proposed changes in shadow environments before they reach users. The technology also connects changes in agent behavior to measurable business outcomes, giving teams more context than a standalone quality score.
Strengthening Rootly’s Own Agents
The acquisition serves both Rootly’s customers and the company’s internal AI development strategy. Rootly plans to integrate ThinkHive’s groundedness scoring, failure detection, regression controls, and shadow testing into the agents it deploys during high-stakes incidents, where inaccurate recommendations could slow recovery or introduce additional risk. This approach is intended to provide evidence that Rootly’s agents remain reliable after changes to prompts, models, data, and production conditions.
Building More Proactive Incident Management
Rootly also expects ThinkHive’s evaluation engine to support earlier intervention across the software lifecycle. The combined technology could help assess the risk of a code change by comparing it with historical incidents and current telemetry, while also identifying likely failures based on similarities with past events. Rootly said this evidence-driven approach should improve how its agents identify probable root causes and propose corrective actions for human responders.
ThinkHive’s Founding Team Joins Rootly
ThinkHive was founded by Nour Alkhatib and Abdulwahab Omira to address reliability challenges associated with production AI agents. Alkhatib previously led AI products at Instacart, where repeated investigative work was required to understand why agents serving large numbers of customers were not consistently producing the expected business results. At Rootly, the ThinkHive team will lead work on agent reliability while maintaining an approach that keeps experienced engineers involved in critical decisions.
The acquisition reflects a broader shift in software reliability as companies move AI agents from experimentation into customer-facing and operational roles. Rootly is positioning ThinkHive’s observability and evaluation capabilities as a foundation for making AI-assisted incident response more measurable, testable, and accountable. The success of the integration will depend on whether the combined platform can consistently identify silent AI failures and help engineering teams act on them before they escalate.