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Tag: Gremlin

  • Gremlin Launches Foresight AI to Proactively Fix Reliability Risks

    Gremlin Launches Foresight AI to Proactively Fix Reliability Risks

    The pioneers of Chaos Engineering built Foresight AI on top of a decade of real-world data around how systems fail and recover. Gremlin Foresight AI identifies and fixes reliability concerns before they become incidents, empowering enterprises to move confidently at AI speed.

    DENVER, CO — October 7, 2026 — Gremlin, the company founded by Amazon and Netflix engineers that pioneered Chaos Engineering, today announced the general

    availability of Gremlin Foresight AI. Following a successful beta, Gremlin Foresight AI has proven it can identify and address reliability risks before they become incidents, helping engineering teams move at AI speed without compromising the resilience of mission-critical production systems.

    “AI-driven development means shipping code at 10X velocity; it also means 10x the opportunity for bugs, risks, and failures,” said Kolton Andrus, CEO and Founder of Gremlin. “While the rise of AI SRE tools are great for helping teams respond to incidents faster, it’s still cleanup after something breaks. Gremlin Foresight AI finds risks proactively, delivers the fix, and verifies the fix worked by rerunning the test. This provides teams with the assurances needed to move confidently.”

    Gremlin Foresight AI is built on top of the company’s proprietary Failure Atlas, which incorporates more than a decade of cause-and-effect data around how online systems fail. This specialized foundation means every recommendation is grounded in real-world failure patterns and operational experience, not generic best practices. Every fix is validated against the test that surfaced the risk, closing the loop between identifying a weakness and proving it’s resolved.

    Key features of Gremlin Foresight AI include:

    • Proactive risk detection: Identify potential weaknesses and failure conditions before they become incidents.
    • Guided remediation: Recommend and deliver the specific fix as a configuration patch or infrastructure-as-code change.
    • Continuous validation: Re-run the originating test to confirm each fix performs as intended, and repeat testing as systems change.
    • Measurable resilience: Track reliability scores across services and teams to quantify improvement and prioritize further investment.

    Before founding Gremlin, Kolton Andrus was responsible for the uptime of Amazon’s retail site. He then joined Netflix to build the company’s second generation of fault-injection tooling, following the popularity of their open source project Chaos Monkey. Gremlin has since pushed the discipline of Chaos Engineering beyond one-off fault-injection experiments into a comprehensive approach to world-class reliability management.

    The Gremlin platform enables teams to run planned experiments, control the blast radius, and use continuous automated testing to verify that fixes remain effective over time. Reliability scores make that progress measurable across the whole organization, giving every team a shared standard to work toward, as well as giving leaders the visibility to direct investment and hold teams accountable.

    “Gremlin has always tried to help engineering teams improve the resiliency of their applications by running experiments proactively to identify weaknesses in their distributed systems, but many teams didn’t have the time or expertise to do that consistently,” said Mike Dauber, General Partner at Amplify Partners. “Foresight AI is like a trainer who does the reps for you. Your systems get stronger without your team doing all the manual work.”

    Gremlin Foresight AI extends their proactive philosophy into today’s AI-driven world, delivering agentic resilience by automatically identifying reliability risks earlier and automating fixes where appropriate to avoid outages, degraded services, and negative customer impact. Gremlin Foresight AI is generally available starting today. To learn more or request a demo, visit gremlin.ai.

    About Gremlin

    Gremlin is the reliability management platform trusted by the world’s largest enterprises to build resilience across everything they ship and run. Gremlin combines failure testing, passive risk detection, dependency mapping, and agentic resilience to give engineering teams the predictive data they need to systematically measure, manage, and improve their reliability. Learn more at gremlin.com.

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  • Gremlin Adds Detected Risk Tool to Chaos Engineering Service

    Gremlin Adds Detected Risk Tool to Chaos Engineering Service

    Gremlin today added a risk detection capability to its chaos engineering service that automatically identifies issues that could cause outages along with recommendations to resolve them.

    Enabled by agent software deployed in an IT environment, the Detected Risk tool makes it possible to benefit from chaos engineering techniques without needing to construct experiments or run reliability tests.

    Gremlin CTO Kolton Andrus said that approach eliminates much of the friction that previously limited adoption of chaos engineering best practices to improve the resiliency of IT environments.

    Chaos engineering is rooted in a resiliency testing philosophy that posits applications should be able to keep functioning regardless of any service or infrastructure failure. Most IT organizations, however, don’t have the expertise required to build the tools required to test resiliency based on the “Chaos Monkey” principles—pioneered by a Netflix project by that name—that, for example, randomly turns services off to see if an application fails.

    Each DevOps team will need to evaluate Gremlin’s recommendations before implementing them, but the overall goal is to provide IT teams with a better understanding of the level of risk attached to, for example, a misconfiguration of a server. To do so, the tool uses a scoring mechanism provided by Gremlin, noted Andrus.

    The challenge is that as IT environments become more complex, the level of dependencies between applications and infrastructure continues to increase. It’s often impossible for DevOps teams to manually keep track of all the hidden dependencies that might exist when IT environments are continuously being updated.

    In theory, of course, adopting cloud-native applications based on microservices should make IT environments more resilient as calls to application programming interfaces (APIs) are rerouted in the event of an outage. Nevertheless, there are almost always dependencies that get overlooked as these applications evolve, which creates a potential single point of failure.

    Naturally, not every IT team is always comfortable with the concept of deliberately breaking things to better understand what issues might cause an outage. As organizations become more dependent on software to drive critical processes, however, a more proactive approach to ensure resiliency is required, added Andrus.

    In fact, business leaders are becoming less tolerant of outages that result in lost revenue, so DevOps teams are being held to a higher level of accountability. DevOps teams may appreciate the value of a blameless culture that encourages everyone to learn from their mistakes, but IT leaders still get fired when there are too many outages, noted Andrus.

    Less clear is the degree to which the rise of artificial intelligence might affect chaos engineering. At the very least, AI models should make it easier to assess levels of risk, but the cost of using those technologies to achieve that goal is substantial, especially when platforms that provide the same outcome already exist.

    Regardless of approach, however, when it comes to resiliency, everyone agrees that the value of application uptime is now beyond measure.