Cleaner pull requests, first time
Routine AI mistakes are fixed before code reaches review. Reviewers spend their time on logic and architecture — not cleanup.
SonarQube Agentic Analysis
Verify AI code before you commit
SonarQube Agentic Analysis verifies code written by AI agents against your team's quality and security standards while the AI is still writing. Bugs get fixed in seconds — not hours later in code review.
Works with AI coding agents your team uses
AI is fast. Verification should be too.
Basic local linter checks are fast, but too shallow. Pull request review and CI are trusted, but too late for teams to discover routine AI-generated issues. That creates avoidable rework, review churn, and lower confidence in AI-assisted development.
Linters miss the real issues
Basic code checkers only look at one file. They miss bugs that appear when different parts of your codebase interact.
CI feedback arrives too late
By the time CI flags a problem, the developer has moved on. Switching back to fix it costs time, focus, and momentum.
Reviewers spend time on AI cleanup
Senior engineers and security teams waste review cycles catching routine AI mistakes instead of focusing on architecture and logic.
Every AI tool has its own rules
Without a shared standard, teams using multiple AI coding tools get inconsistent code quality across the same codebase.
Capabilities
More than a linter. More than a security scanner.
Agentic Analysis applies the full depth of SonarQube's analysis — the same coding standards your teams trust in CI — directly inside the AI coding workflow.
Real-time, pre-PR verification
Issues are caught and fixed during code generation — not hours later when a developer has to stop what they're doing to clean up.
Full project context, not just one file
Uses cached data from your previous CI builds to understand how your entire codebase connects. Catches cross-file bugs that single-file checkers miss.
Your standards, automatically applied
No new rules to define. Agentic Analysis uses the quality profiles your team already enforces in SonarQube — across every AI tool on the team.
Security and code quality together
Checks for vulnerabilities, reliability issues, maintainability problems, and exposed secrets — not just security, and not just style.
Works with the tools your team already uses
Connects through the SonarQube MCP Server to Cursor, GitHub Copilot, Claude Code, Windsurf, Gemini CLI, and any MCP-compatible workflow.
Consistent across your whole AI stack
One verification standard for every AI coding tool on the team. No more inconsistent code quality when developers use different assistants.
How it works
Write. Verify. Fix. Ship.
Agentic Analysis works inside the AI's coding loop — before the developer sees anything or opens a pull request.
Guide
1. Set the context
Sonar Context Augmentation gives the AI agent your project's quality rules and code context before it writes a line.
Generate
2. AI writes code
Your AI coding tool — Cursor, Copilot, Claude Code, or any MCP-compatible tool — generates code as it normally would.
Verify
3. Sonar checks it
Agentic Analysis automatically checks the code against your SonarQube quality profiles using full project context — in seconds.
Solve
4. AI fixes and re-checks
The AI uses Sonar's specific, rule-based findings to fix its own mistakes and re-verify — before the developer ever sees the code.
Single-file feedback is not enough
Many meaningful issues cannot be detected from a single file alone. A change that looks correct in isolation may still depend on a deprecated API, unsafe usage pattern, missing type relationship, or broader project logic. Agentic Analysis uses SonarQube project context to make fast feedback accurate enough to trust.
- Evaluate changes beyond the current file.
- Apply the team's existing SonarQube standards earlier in the workflow.
- Improve first-pass pull request quality without replacing CI or review.
Business outcomes
What changes for your team
Cleaner pull requests, first time
AI productivity your organization can trust
Less rework, more focus
One standard across every AI tool
Our differentiation
Why Sonar is a strong fit for the workflow.
Agentic Analysis extends the SonarQube verification teams already trust in CI into the moment AI-generated code is created, where issues are cheapest to catch and easiest to fix.
Proven analysis engine
Built on the same Sonar analysis foundation teams already use to improve code quality and code security.
Project-aware verification
Bring SonarQube context, baseline, and standards into the agent loop instead of relying only on local heuristics.
Earlier issue removal
Catch and correct routine AI-generated issues before they become reviewer cleanup, not after.
Get started
Your AI writes the code.
Sonar makes sure it's ready to ship.
See how Agentic Analysis fits into your team's existing AI workflow — no new tools to learn, no new standards to define.
Frequently asked questions
SonarQube Agentic Analysis is a real-time code verification service from Sonar. It connects AI coding tools — such as Cursor, GitHub Copilot, and Claude Code — to SonarQube's analysis engine through the Model Context Protocol (MCP). While the AI is writing code, it automatically checks its own output against your team's SonarQube quality and security standards, finds issues, fixes them, and re-checks — all before the developer opens a pull request.
