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Weights & Biases Builds Agent to Hunt Silent Logic Bugs

Weights & Biases, an AI developer platform for tracking LLM applications, built an autonomous bug-finder agent using the Gemini Managed Agents API. The tool targets "silent" bugs—logical flaws where code runs cleanly and passes unit tests but produces incorrect outputs. It uses the Antigravity agent harness to reason across entire repositories.

This implementation addresses the reliability gap in agentic coding workflows. As developers move from single-turn chat to autonomous swarms, the risk shifts from code that crashes to code that quietly fails. This agent parallels high-reasoning verification tools like Claude Code Ultrareview that audit AI-generated changes before production.

You can use the agent to audit repositories by providing a codebase for scanning. Every action—including the agent's internal reasoning, executed code, and observed outputs—is traced in W&B Weave. This ensures that long-running autonomous tasks remain inspectable rather than leaving only a final report as the sole artifact of a ten-minute run.

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The scariest bug from a coding agent isn't the one that crashes. It's the one that runs cleanly, passes tests, and quietly produces wrong results. So we built one on @Antigravity's Gemini Managed Agents API to hunt them. Give it a repo. Get back the bugs that passed review. https://t.co/fgAUN6OHas

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Still wondering? A few quick answers below.

The bug finder is an autonomous agent designed to identify logical errors in software repositories. Unlike standard testing tools that catch crashes, this agent hunts for silent bugs that run cleanly and pass existing reviews but produce incorrect results. It uses high-level reasoning to audit codebases for these subtle flaws that human reviewers often miss.

The agent is built on the Gemini Managed Agents API and uses the Antigravity agent harness to perform multi-step reasoning across a repository. It executes code, observes outputs, and generates reports. Every step of this process is recorded in W&B Weave, allowing developers to inspect the agent's internal reasoning and the specific actions it took during its run.

W&B Weave acts as an observability layer that traces every step of the agent's execution. When an agent runs autonomously for extended periods, Weave captures the reasoning, the code executed, and the resulting outputs. This ensures that the final report is not the only artifact, making the entire autonomous process transparent and easier for developers to debug.

The bug finder agent is powered by Google's Gemini models through the Gemini Managed Agents API. This infrastructure allows the agent to operate in a secure cloud sandbox while performing complex tasks like file operations and tool use. By using this managed API, the system can handle the orchestration and state management required for deep code analysis.

Traditional scanners typically rely on static patterns to find syntax errors or known vulnerabilities. This agent uses the reasoning capabilities of large language models to understand the intended logic of a program. It specifically targets bugs that do not trigger errors or crashes, providing a secondary layer of verification for code that has already passed standard automated tests.

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