Operations | Monitoring | ITSM | DevOps | Cloud

Troubleshooting complex cloud environments just got a whole lot easier. With Lumigo Copilot Beta, we’re redefining how developers identify and resolve issues in their production environments. We’ve captured it all in an exclusive video demo, showing you exactly how this cutting-edge tool empowers developers to stay in control.

AI is changing our world, and its impact on observability is no different. This article discusses some of the components of a good observability platform, how AI is well-positioned to revolutionize observability, and how Lumigo Copilot Beta will provide substantial value to customers and partners.

Artificial intelligence is reshaping industries at an unprecedented pace. AI has found its way into almost every vertical, from writing code to diagnosing illnesses, promising efficiency and innovation. The idea of an AI Copilot—a tool that acts as your assistant to tackle complex tasks—is particularly exciting. In our space, observability, the possibilities seemed endless. We asked ourselves how AI could simplify troubleshooting in microservices.

Generative AI (GenAI) is revolutionizing industries by driving innovation and delivering unparalleled customer value. Across sectors, its impact is evident.

We’re excited to announce Lumigo Metrics, the latest addition to Lumigo’s industry-leading observability suite. Developers already rely on Lumigo for the most advanced distributed tracing on the market, coupled with powerful log management capabilities and the AI-driven insights of Lumigo Copilot Beta—empowering teams to troubleshoot faster and smarter. Now, we’re taking it a step further.

In September, Lumigo announced we were exploring how AI can help shape the next generation of observability. Since then, we’ve unveiled the beta of Lumigo Copilot, which we believe will be the most intelligent AI in observability. Today, we’re providing an update on our progress and inviting our customers to participate in the beta.

Lambda’s Event-Source Mapping (ESM) has been a game-changer for Lambda users. It gives users an easy and cost-efficient way to process events from Amazon SQS, Amazon Kinesis, Amazon DynamoDB, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon MQ and more. It handles all the complexities around polling, including scaling the no. of pollers. And there’s no charge for this invisible layer of infrastructure!

Generative AI (GenAI) is still in its infancy, but its impact is already being felt across industries. Over the past year, production applications leveraging GenAI have gone from proof-of-concept to delivering real-world value. According to the World Economic Forum, 75% of surveyed companies plan to adopt AI technologies by 2027. Leading cloud providers like AWS are making significant investments.

Predictive analytics has become a key goal in observability. If teams can foresee potential system failures, performance bottlenecks, or resource constraints before they happen, they can act preemptively to mitigate issues. AI holds the promise of making this possible. In this post, we explore how AI can push observability toward predictive analytics, the industry’s current hurdles, and practical use cases for leveraging AI today.

Serverless is an incredible paradigm, but performance tuning sometimes feels like a black box. You have no control over the infrastructure, but that doesn’t mean you can’t optimize. In this post, let’s look at five ways to take serverless performance to the next level.