GlassBox AI FAQs

Question 1

What is GlassBox AI and what does it do?

Accepted Answer

GlassBox AI is an autonomous coding agent designed to take a GitHub issue and automatically generate a tested pull request. It focuses on building trust infrastructure for AI agents by making every decision visible and accountable.

Question 2

How does GlassBox AI ensure reliability and transparency?

Accepted Answer

It uses a multi-agent debate pipeline with distinct roles (Manager, JuniorDev, Tester), an adaptive trust scoring system for each agent, and provides a full reasoning chain in every PR. Human-in-the-loop (HITL) mechanisms allow for author guidance and intervention.

Question 3

What kind of code changes can GlassBox AI handle?

Accepted Answer

GlassBox AI is proficient in generating fixes for various issues, leveraging specific templates like typo fixes, wrong values, wrong names, and swapped arguments. It uses precise line-number editing for robust code modifications, avoiding common 'string not found' errors.

Question 4

How does GlassBox AI learn and improve over time?

Accepted Answer

The system incorporates a Reflexion memory, allowing agents to learn from past verbal failure reflections. Additionally, its adaptive trust scoring system adjusts based on agent outcomes, continuously improving performance and reliability across sessions.

Question 5

How can I integrate GlassBox AI into my development workflow?

Accepted Answer

You can integrate GlassBox AI by labeling a GitHub issue `glassbox-agent` or mentioning `@glassbox-agent`. It also functions as an MCP (Multi-Agent Communication Protocol) server, allowing your AI assistant to leverage its tools automatically once installed.

GlassBox AI

GlassBox AI

Key Features

Use Cases

Key Features

Use Cases