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
Point an agent at a running application and it crawls – reading screens, following links, filling forms – and builds a knowledge graph of the product. From that it proposes test cases as goals in plain language: "a new user can sign up and reach the dashboard." You review the list, keep what matters, delete what doesn't, and edit the ones that are close. What you still decide is which cases matter: an agent can tell you everything the product makes possible, not which of those things would cost you a customer. That judgement stays with you, and it is the part worth your time – it is also the first of the four decisions in our guide to software testing strategies.
ReadAPI Regression Testing
You group API contract checks into a plan written as plain-language goals – "GET /orders returns 200 with a list, and every item has an id and a status" – and an AI agent runs them against your API environment on every deploy. The agent makes the requests, writes and executes its own validation code, and returns a verdict with the full request and command trace.
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. Many teams wire this into PR testing so the suite runs on every pull request preview. 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. This is the workflow behind our automated web testing product.
Read
Pull Request & CI/CD Testing
Next →SaaS Application Testing (vertical)
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