How Google uses AI

The Chrome security team uses Gemini based agents across the life of a security bug. An automated triage pipeline filters spam and duplicates, reproduces bugs, adds severity and routes them to the owner; fixing agents propose candidate patches that a critic agent reviews, and test writing agents add tests before a developer evaluates the fix. Chrome fixed 1,072 security bugs in milestones 149 and 150, more than the prior 23 milestones combined, and Google says LLMs now generate candidate fixes for most vulnerabilities. It estimates the triage automation saves hundreds of hours of developer time a month.

No outcome disclosed.

Google used an internal LLM based system to help engineers with large scale code migrations, such as changing identifier types from int32 to int64. Engineers doing the migrations estimated the total time spent was reduced by about 50%, and reported that 80% of the code changes in landed changelists were AI authored, with the rest written by humans.

• Cycle time reduction: about 50%

  "The total time spent on the migration was reduced by an estimated 50% as reported by the engineers doing the migration."

  Claimed by: organization

Google site reliability engineers use an agent in the Gemini CLI across an outage: reading the page, investigating, proposing mitigations, finding the root cause and drafting the postmortem. Every proposed change passes a policy layer (for example rules that need two person approval) and a forced human confirmation, and every proposal and approval is logged. No outcome figures are published.

No outcome disclosed.

Google's security engineering team built a pipeline that prompts Gemini to generate code fixes for bugs that sanitizers find during unit tests in C and C++, Java and Go code, such as uninitialised values, data races and buffer overflows. Every generated fix goes to a human reviewer before it lands. Google reports that the pipeline fixed 15% of these bugs, hundreds in total, and expects the rate to improve.

No outcome disclosed.