Show HN: Wild Moose – Autonomous agent for production debugging

Wild Moose is an autonomous AI agent designed for production debugging that connects to observability data like logs and metrics from Datadog. Rather than focusing on code generation, it performs root-cause analysis by exploring multiple data sources through conversational interfaces, combining code execution with API invocations to answer debugging questions.

Key Points

• •Production debugging is 80% of software development work, yet GenAI tools focus mainly on code generation (20%) — Wild Moose addresses this gap with an autonomous debugging agent
• •The agent performs root-cause analysis (RCA) by exploring multiple data sources: metrics, logs, code, Slack messages, and observability data through conversational interface
• •LLM agent combines two capabilities: running code and invoking APIs together — a non-trivial design challenge that goes beyond existing solutions like Open Interpreter or Gorilla
• •Users can ask natural language questions like 'show IDs of transactions over 1 minute today' and the agent fetches data from Datadog, analyzes it, and visualizes correlations automatically
• •The tool integrates with Datadog observability platform and Slack, enabling teams to debug production issues without context-switching or manual data exploration
• •GenAI's strength in data exploration and unstructured data analysis makes it ideal for debugging workflows where the root cause is unknown and requires investigating multiple data types
• •Agent reasoning loop: receive task → reason about approach → invoke APIs → run code → analyze results → return answer — enabling iterative debugging conversations

Workflow Diagram

Concepts