Wild Moose

Yesterday I met Yasmin Dunsky of Wild Moose. They are building autonomous investigation and debugging agents to help SREs triage and resolve incidents. They made great progress since we last met. Quite a few companies are using them to reduce the load and time to solve production issues. If you are an SRE or manager - Reach out to Yasmin and check Wild Moose out!

We had a big breakthrough today: in just 3.5 minutes, Wild Moose identified the root cause of an issue that took a team of engineers half a day to diagnose. The speed and precision were way beyond our expectations. Being a founder is a constant grind, but a moment like this of true innovation is an energy boost that reminds us why we keep pushing forward. This feels big. And we’re only warming up. 😌🙌

Is "looking for new AI tools" just a Solution in Search of a Problem? We’re seeing frustration from employees when management pushes to “add AI”. It is tempting to criticize management here, but their job is not always easy: failing to modernize can stifle progress, while chasing after every shiny new product can waste valuable time and money. So, how do you strike the right balance? It’s about separating real value from hype, which is often easier said than done. The key is to focus on three crucial elements: 1. A new solution 2. To an old, stubborn problem 3. With a compelling “why now” When these three elements align, it often signals a true disruption—a known problem finally being tackled in a way that was previously impossible. My advice for managers: When evaluating new tech or speaking with entrepreneurs, challenge the “why now.” Look beyond superficial enhancements to understand if the technology genuinely addresses a core aspect of your problem in a novel and impactful way.

We've all been there - frantically fixing a bug that somehow slipped through the cracks and made it into production. Was it a rushed deadline? A complex feature? Or maybe something else entirely?  Vote and share your own war stories in the comments. 

Automation of incident response is where the industry is heading. We're seeing response times slashed from hours to minutes as AI triages issues, analyzes impact, and suggests next steps - all before human intervention is needed. And this is just the beginning. As models continue to improve, managing incidents without these tools will soon become unthinkable. Yet, we often hear misconceptions about companies' readiness to adopt these solutions: ☝️ “Our logs are messy” - This isn’t a problem. In fact, it's where Generative AI excels. Large Language Models thrive on unstructured data. ☝️ “We use legacy observability tools and are mid-migration to a new one” - Great automation tools are observability-stack agnostic. They create an abstraction layer, integrating with any obscure tool in the background. ☝️ “Our runbooks and playbooks are outdated” - Yes, that is the case for 99.999% of companies we talk to. LLMs can leverage data such as Slack history and post-mortems to automatically maintain playbooks with minimal manual intervention. Hit us up if your organization is ready to take incident response to the next level.

Summer swag is here! Gotta love the Wild Moose. 😎

🌟 Thank you for the support and for recognizing our team's hard work. We're thrilled to be acknowledged in such an exciting space!

Meet our moose, an AI software engineer exclusively dedicated to production debugging, who ensures your team's focus remains on innovation without the weight of operational stress. 💬 Bring this to your team >> https://lnkd.in/dBfG3u_j Consider this nightmare scenario for any engineer: you wake up at 2am to discover the application is down. Your company is bleeding money, you're only half awake, with minutes to act before alerting other teams, and it's all on you. Until recently, the burden that engineers at all levels face in situations like this was an unavoidable reality. Recent progress in generative AI opens a door to a whole new reality. Imagine that your checkout page is suddenly experiencing delays. An AI agent – let's say in the form of a moose 😏 – gets to work analyzing recent deployments to identify anything suspicious. It checks for system load issues, explores metric trends, and delivers all of this information directly to your Slack space. Within just a few minutes, you’ve figured out the extent of user impact and see a potential fix. An organization-wide escalation is prevented. This reality is at hand. Let's chat about how we can make this work for you! Wild Moose Tom | Roei | Einat | Avishay | Reut

We're thrilled to be mentioned alongside innovative YC companies in the AI developer tools space! Try us out by adding us to Slack: https://wildmoose.ai

Introducing Moose Force, at your service and ready to charge at your production bugs. 🫡🔥 Our AI agents are now designed to tackle production bugs even before they catch human eyes. For any alert, new bug ticket, or user issue report, multiple moose agents spring into action, immediately initiating the investigation. By the time a developer gets to work on an issue, there is already a wealth of relevant information readily available. With the support of our multi-agent technology, ensuring stability, your developers are free to dedicate their efforts to launching new features. Add to Slack and give it a try >> https://wildmoose.ai Chat with us to explore this for your team >> https://lnkd.in/eeQFcEgU