In a digital landscape increasingly shaped by rapid deployment and AI-assisted development, maintaining system reliability is becoming both more critical and more complex. Gremlin, a longtime leader in Chaos Engineering, is stepping into this challenge with the launch of Reliability Intelligence—a new AI-powered solution aimed at helping organizations proactively identify, analyze, and resolve reliability risks in real time.
The new product, announced today, combines automated fault injection, continuous resilience analysis, and integration with large language models (LLMs) through a proprietary Model Context Protocol (MCP) server. The result is a deeply integrated system that allows businesses to reduce downtime and improve performance across increasingly dynamic software stacks.
“The Gremlin team has been managing complex online systems for decades,” said Kolton Andrus, CEO of Gremlin. “We know that you can’t just throw LLMs at the hard engineering problems involved with building and maintaining business-critical systems. Reliability Intelligence will provide actionable recommendations based on a deep understanding of your systems architecture and its dependencies across various cloud providers and third-party services.”
AI-Powered Reliability, Grounded in Real Engineering
Gremlin’s move comes as companies accelerate software deployment cycles with the help of AI. According to the latest DORA (DevOps Research and Assessment) report, teams are now shipping code to production 70% faster thanks to AI coding assistants. But with that speed comes risk: AI-generated code is often error-prone and difficult to debug, increasing the potential for outages.
Traditionally, practices like Chaos Engineering have offered a solution—but they require specialized expertise that’s still relatively rare. Gremlin’s answer is to lower the barrier to entry, making proactive reliability more accessible and automated.
Recent features like Reliability Scoring, Intelligent Health Checks, Dependency Discovery, and Executive Reporting have already moved the platform in this direction. With the addition of Reliability Intelligence, Gremlin is aiming to make proactive reliability a default, rather than an elite practice.
Key Capabilities in the New Release
- Experiment Analysis: Automatically compares test outcomes to expected behavior using LLMs. It can detect anomalies, understand test context, and determine pass/fail status—previously a manual task.
- Recommended Remediation: After identifying a failure, the system offers engineers specific, actionable fixes drawn from a library of best practices and millions of past test results.
- MCP Server: Enables LLMs to query telemetry and trace data directly. Users can generate insights or build dashboards using plain language—bringing powerful observability tools to a wider set of users.
“In high-velocity environments, reliability can’t be an afterthought,” said Arul Martin, Director of Performance Engineering at Sephora. “Reliability Intelligence equips SRE and performance teams with deep, real-time insights from telemetry and trace data — enabling early detection of reliability regressions, faster root cause isolation, and proactive remediation without disrupting release velocity.”
A New Era of Reliability Engineering
As businesses increasingly rely on AI to accelerate development, the challenges associated with maintaining the health and performance of online systems have never been greater. Gremlin is positioning Reliability Intelligence as a critical piece of the modern SRE toolset, blending helpful AI guidance with the rigor of battle-tested engineering.
For modern teams navigating complex environments, the ability to test, understand, and improve system resilience continuously is no longer a luxury—it’s a necessity that modern teams have accountability and keep the guardrails on.
The post Chaos Engineering Pioneer Gremlin Launches Reliability Intelligence appeared first on International Business Times.
