How do you test AI-generated code?
AI coding tools ship faster but don't always see the cross-cutting effects of a change, so bugs accumulate faster too. QA.tech adds the quality layer: connect it via MCP and your coding agent (Claude Code, Cursor, Codex or Continue) can trigger tests, read results, and fix issues – an agentic feedback loop for AI-built features. The `qatech init` command generates Claude Code subagent and skill files directly in your repo, so the build-test-fix loop is usable immediately.
Sub-use-cases
Covers MCP-triggered test runs, agentic build-test-fix loop, QA for vibe-coded apps, catching emergent cross-cutting bugs and QA as a node in an agentic SDLC.
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What this protects against
Cross-application regressions from AI changes, an automated build → test → fix loop, and QA as a composable agent in an AI-native pipeline.
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How does the build-test-fix loop work?
Via the MCP integration, a coding agent calls QA.tech to test a new feature, receives structured results, and acts on them – closing the loop with little or no human step.
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When to add a quality layer for AI code
When AI tools generate a meaningful share of your code and you need a systematic quality gate.
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Who needs QA for AI-generated code
AI-native teams on Lovable, Cursor, Claude Code, and platform teams building agentic pipelines.
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How QA.tech helps
The risk with AI code isn't that it looks wrong – it's the unseen effects elsewhere. QA.tech catches those emergent, cross-cutting bugs and plugs into the agent loop via MCP.
FAQ
Common questions
- What is MCP and why does it matter here?
- It lets coding agents talk to QA.tech directly – triggering runs and reading results inside their own workflow.
- Which coding agents are supported?
- Claude Code, Cursor, Codex and Continue.
- Does this replace human review?
- It automates the test-and-fix loop; humans still own judgment and sign-off.
Related use cases
AI Test Generation
QA.tech generates tests five ways, so coverage can grow from wherever your team already works: a plain-language description, a crawl of your app, an issue tracker, your pull requests, or the API. You review and approve rather than author from scratch, which removes the slowest step in growing coverage.
ReadAutomated Regression Testing
Regression testing re-checks that existing features still work after a change. To automate it with AI, you group tests into a regression plan written as plain-language goals, and agents run the whole suite in parallel on every deploy. A 50-test suite that took hours by hand finishes in around ten minutes, and the tests don't need rewriting when the UI shifts.
ReadExploratory Testing
Exploratory testing means investigating an application to discover how it behaves and where it breaks, rather than following a fixed script. AI can do this: an agent clicks through a new or changed feature, finds the flows and edge cases, and proposes test cases for what it sees. With QA.tech this runs automatically when a feature lands.
Read
Pull Request & CI/CD Testing
Next →SaaS Application Testing (vertical)
Your code ships daily. Can your testing keep up?
QA.tech agents test your product autonomously, so moving fast never means shipping broken. See it run on your own app in a 30-minute demo.