> Markdown version of [/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray?t=632](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray?t=632). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Improving quality with Agentic AI with Rovo Dev and Xray

Stop letting AI assistants inject unchecked vulnerabilities into your codebase. Discover how Agentic AI orchestrates Rovo Dev and Xray to autonomously hunt security flaws right from your terminal.

- **Speakers:** [Sergio Freire](https://www.wearedevelopers.com/@sergio-freire)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 51:27
- **URL:** https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray

## Summary

Traditional software development often relies on a linear, siloed approach where developers write code and testers validate it later. Under time pressure, this leads to a narrow focus on functional correctness, leaving critical dimensions like security, usability, and maintainability vulnerable. As AI accelerates code generation, the risk of embedding unchecked vulnerabilities or unreviewed dependencies increases, necessitating a shift toward continuous, whole-team quality orchestration.

Agentic AI transforms this dynamic by operating autonomously to plan, adapt, and pursue multidimensional quality goals without forcing developers out of their primary environments. By integrating Atlassian RovoDev CLI with Jira, Xray test management, and tools like SonarQube via the model context protocol, development teams can evaluate requirement coverage, automate manual tests, and trace user stories directly from the terminal. AI assistants can instantly map code against testing status, generating unit tests for Spring Boot endpoints while automatically annotating them for Jira traceability.

True value emerges when AI is used not just to write code, but to orchestrate multiple sub-agents that review pull requests from diverse analytical angles simultaneously. This capability uncovers hidden security flaws—such as mass assignment vulnerabilities—and can automatically log detailed bug reports in Jira or draft Confluence architecture documents. Furthermore, rendering complex test coverage and security metrics through interactive 3D visualizations makes codebase health instantly intuitive. Ultimately, AI should not replace human scrutiny but augment it; developers must critically evaluate AI outputs and leverage models to build reusable automation scripts rather than relying on real-time generation for repetitive tasks.

**Keywords:** agentic AI, software testing automation, rovodev CLI, xray test management, jira issue tracking, model context protocol, requirement coverage analysis, automated bug reporting, multidimensional code review, sub-agent orchestration, 3d codebase visualization, sonarqube integration, manual test automation, continuous quality integration, code vulnerability analysis

## Chapters

1. **Moving away from the linear software development mindset** (00:01) — Traditional linear workflows often lead to skipped testing and a narrow focus on functional correctness.
1. **Defining multidimensional quality in software development** (04:07) — Software quality encompasses usability, security, and maintainability which vary by context and user needs.
1. **Evaluating the flaws and benefits of AI code generation** (07:01) — While AI can introduce vulnerable dependencies, advanced agentic models can effectively uncover legacy security issues.
1. **Accelerating development with the RovoDev AI coding assistant** (10:32) — Integrating an AI coding assistant within the command line removes friction and provides comprehensive project insights.
1. **Utilizing memory, skills, and prompts in the RovoDev CLI** (15:30) — Storing project context and leveraging custom skills enables AI agents to query external tools without context switching.
1. **Integrating MCP servers for legacy tool connectivity** (18:54) — Connecting the AI CLI to generic MCP servers enables interaction with legacy GraphQL endpoints and external quality analysis tools.
1. **Assessing sprint requirement coverage from the terminal** (22:35) — Querying test management tools directly from the CLI reveals unaddressed sprint items based on actual test coverage.
1. **Automating feature implementation and GitHub pull requests** (28:26) — AI agents can draft API endpoints, write integration tests, execute test suites, and generate code reviews autonomously.
1. **Identifying vulnerabilities and automating Jira bug reports** (36:02) — Multiple AI sub-agents analyze code for security issues and automatically generate detailed tickets and system documentation.
1. **Organizing manual tests and visualizing code quality metrics** (41:21) — AI agents can reorganize existing manual test cases and generate interactive 3D maps to visualize code coverage.
1. **Best practices for applying AI to software engineering** (46:18) — Generative AI should augment human decision-making and generate reusable automation scripts rather than replacing engineers completely.

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- [Building AI agents for software development life cycles](https://www.wearedevelopers.com/videos/100145-5-things-i-wish-i-hadn-t-done-building-my-ai-agent) (from "5 things I wish I hadn’t done building my AI agent")
- [Leveraging AI for development workflows and collaboration](https://www.wearedevelopers.com/videos/1623-breaking-silos-successful-collaboration-between-tech-business-teams-in-complex-enterprise-systems) (from "Breaking Silos: Successful Collaboration Between Tech & Business Teams in Complex Enterprise Systems")

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