Wild Moose – Autonomous agent for production debugging
Hi Hacker News! We launched an autonomous agent that helps debug production issues, and we’re curious to get your feedback. Today's GenAI devtools, such as Copilot, are limited: they are great for writing code, but we all know that programming is only 20% coding, and 80% debugging. So how can GenAI be used for debugging? As opposed to code completion or test automation, production debugging is not about generating text. Debugging is mostly about root-cause analysis. We realized two things: 1) Generative AI is drastically changing the way we work with data, thanks to its ability to not only…
In plain words
Wild Moose is an autonomous agent designed to help software teams debug production issues by performing root-cause analysis. Unlike general coding assistants, it focuses on the debugging phase of development by exploring and analyzing various data types to identify why problems occur. The tool uses generative AI to run code, query data, and examine unstructured information, providing an intuitive interface for developers investigating production failures, customer impact, and system anomalies.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi Hacker News! We launched an autonomous agent that helps debug production issues, and we’re curious to get your feedback. Today's GenAI devtools, such as Copilot, are limited: they are great for writing code, but we all know that programming is only 20% coding, and 80% debugging. So how can GenAI be used for debugging? As opposed to code completion or test automation, production debugging is not about generating text. Debugging is mostly about root-cause analysis. We realized two things: 1) Generative AI is drastically changing the way we work with data, thanks to its ability to not only generate queries, but also run code and analyze unstructured data. This enables building better data-exploration experiences with far more intuitive interfaces. 2) RCA is all about exploring different types of data. When you don’t know why a transaction was dropped or which customers are affected – you explore metrics, logs, your code, other people’s code, old slack messages, and whatnot, to figure out what’s broken. Putting those two together, we built an autonomous agent that helps debug production issues. Our LLM "moose" (ok, it's corny but we like it) connects to your logs, metrics, and other observability data, and lets you extract and analyze them by conversing with it. Once it gets a task, it will start reasoning, invoking APIs, and running code, until it comes back with an answer. For example, when requested to “show me IDs of transactions that took over 1 minute today”, it will fetch those transactions from Datadog for you. You might then ask it if long-running transactions correlate with a metric such as DB CPU load. It will fetch the metric values, visualize them on a graph alongside the long transaction frequency, and give you the answer. Our software both runs code and invokes API calls; the interplay between these is nontrivial to design and a fertile ground for innovation. There are “textbook” solutions to let agents write and run code (open sourced by, for example, Open Interpreter), and also to invoke tools/APIs (for example, Gorilla). But doing both together is harder, and yet required. We welcome your thoughts on this! Try our tool with your Datadog’s logs and metrics >> https://app.wildmoose.ai/slack/install Setup demo >> https://www.loom.com/share/9a4adc39806742c48d14cdd39da6e560?... If you want to see other integrations, or have ideas for features, or you’ve spotted behaviors that seem off - we’d love to hear. Hit us up in the comments!
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