# Hunt bugs with your dot

## Executive summary

The video demonstrates how an engineer utilizes a specialized AI agent, referred to as a 'dot,' to automate the collection, triage, and initial investigation of product feedback. By integrating sources like Twitter, Reddit, and internal Slack reports, the dot processes external feedback, identifies major bug themes (e.g., silent stalls, connection problems), and initiates root cause analysis via 'cloud threads.' This process significantly accelerates the engineering workflow by offloading manual data aggregation and preliminary investigation to the AI agent.

## Key takeaways

- Automated Feedback Triage: The dot aggregates feedback from diverse sources (Twitter, Reddit, Slack) and organizes it into a structured, color-coded tracker within ChatGPT Space, allowing engineers to visualize and categorize issues.
- Identifying Major Bug Themes: The AI agent identified key recurring issues in the last two days, including 'silent stalls' (dots appearing to work but never finishing), laptop connection drops, and context loss between voice chat and Codex.
- Prioritizing Fixes: The agent suggested that 'silent stalls' are the most urgent issue, while the simplest fix to implement immediately was the 'greeting model failure'—where a new dot fails if the user cannot access the requested model, instead of falling back to an accessible alternative.

## Technical details

- Bug Investigation Workflow: Engineers can initiate a 'cloud thread' directly from the feedback tracker to request root cause analysis for specific issues (e.g., 'Bad Gateway login errors').
- Greeting Model Failure: The failure occurs when a new dot requests a model the user cannot access, causing the creation process to block. The proposed fix is for the dot to fall back to a usable model instead of failing.
- Data Tracking and Reporting: The dot autonomously updates the feedback tracker, adding new items and providing a detailed investigation timeline for review, confirming a backend greeting failure.

## Practical implications

- Accelerates the bug triage process by automating data collection from disparate sources.
- Allows engineers to offload initial root cause analysis and categorization to AI agents.
- Provides a structured method for tracking and prioritizing technical debt and feature improvements.

## Topics

AI Agents, Software Development Lifecycle (SDLC), Bug Tracking, Workflow Automation, OpenAI Dots, https://learn.chatgpt.com/docs/dots

Source: https://www.youtube.com/watch?v=6f7ojkd4_Wc