# Jina

> Documentation for Jina, the autonomous AI QA engineer for pull requests.

- [Introduction](https://docs.usejina.com/index.md): Jina is an autonomous AI QA engineer that tests important product flows and pull requests for deep runtime issues.
- [Our Approach](https://docs.usejina.com/overview/our-approach.md): Learn how runtime execution, a connected context graph, and an adversarial agent swarm help Jina find deeper issues with fewer false positives.
- [How Jina Works](https://docs.usejina.com/overview/how-jina-works.md): See how Jina turns system context into targeted runtime investigations, validated findings, and a merge score.
- [Configure .jina](https://docs.usejina.com/guides/configure-jina.md): Teach Jina what matters in your repository, how deeply to investigate, and what evidence your team expects.
- [Context layer](https://docs.usejina.com/concepts/context-layer.md): Understand how Jina connects code, infrastructure, runtime behavior, and issue history to reason about change impact.
- [Runtime investigation](https://docs.usejina.com/concepts/runtime-investigation.md): Learn how Jina validates risky behavior by executing targeted probes in isolated environments.
- [Findings and merge score](https://docs.usejina.com/concepts/findings-and-merge-score.md): Understand the evidence Jina reports and how the merge score summarizes review risk.
- [Security](https://docs.usejina.com/security.md): Review Jina's isolated execution, data perimeter, credential, deployment, and audit controls.
- [FAQ](https://docs.usejina.com/faq.md): Answers to common questions about Jina's autonomous pull request reviews, execution model, and security.