Using RL to Double an Agent's Effectiveness in Production Debugging (www.dbow.me)

A recent development at HUD has unveiled a groundbreaking reinforcement learning (RL) environment designed for operational diagnostics, enhancing debugging efficiency in production systems by doubling the effectiveness of agents. By training on 24 real-world production tasks encompassing various error types across platforms such as Sentry, Supabase, Railway, and Kubernetes, the new RL architecture utilizes a composed system of subagents specialized for each tool. This innovative approach prevents the inefficiencies associated with providing a single agent access to all 104 tools, enabling independent training for each subagent tailored to specific environments before they are integrated into a singular orchestrator. This initiative is significant for the AI/ML community as it sets a precedent for developing specialized agents that can operate in complex environments. The method not only improves performance, as evidenced by the trained Sentry subagent outperforming the base model, Gemini 3 Pro, and Claude models in fewer steps, but also lays groundwork for diverse applications in other areas, such as deep research agents and coding assistants. The public release of this RL environment as "cross-service-diagnostics" encourages further exploration and innovation, allowing developers to train custom agents on their own data sets and providing a platform to enhance the capabilities of tool-using agents.