Software testing is no longer a manual checkpoint - it is an autonomous pipeline. BetterQA builds and operates agentic QA systems: AI agents that plan test strategies, generate test cases, execute them across environments, and triage failures without waiting for a human to click "run."
Traditional test automation requires humans to write scripts, maintain them, analyze results, and decide what to test next. Agentic QA inverts this: AI agents observe your codebase, understand changes, generate appropriate tests, run them, interpret failures, and report only what matters.
The difference is not incremental - it is architectural. Your QA team shifts from "running tests" to "governing quality."
Agent monitors repository for commits, PRs, and deployments. Understands what changed and its potential impact.
Maps code changes to affected features, APIs, and user flows. Prioritizes what needs testing.
AI generates new test cases for changed code. Updates existing tests. Removes obsolete ones.
Runs tests across browsers, devices, and environments simultaneously. Scales elastically.
Classifies failures: real bugs vs. flaky tests vs. environment issues. Groups related failures.
Routes bugs to the right team/person based on code ownership and expertise. Creates tickets automatically.
Agent learns from feedback: which tests are valuable, which failures were false positives, what patterns matter.
AI-powered test management that generates test cases from requirements, analyzes release readiness, and creates bugs from screenshots. Integrates with your CI/CD via MCP protocol.
bugboard.create_bug_from_screenshot()
→ AI analyzes visual + context
→ Generates title, steps, severity
→ Creates ticket in your tracker
Record browser tests with Chrome extension, execute them anywhere. Self-healing selectors adapt to UI changes. Data-driven testing from single recordings.
flows.run_suite("checkout")
→ Detects broken selectors
→ Auto-fixes using AI
→ Continues execution
V4 Maximum Coverage AI security scanning. SAST, SCA, DAST, and secrets detection with cross-pollination between agents for comprehensive vulnerability discovery.
security.scan(target, coverage="maximum")
→ SAST + SCA + DAST agents
→ Cross-pollinate findings
→ Attack chains + remediation
Tests run automatically on every commit. No more "waiting for QA to finish." Deploy when code is ready - the pipeline validates continuously.
AI generates tests for new code automatically. Coverage increases with every PR. No more coverage debt accumulating in corners of your codebase.
Your QA engineers focus on exploratory testing, edge cases, and quality strategy - not fixing broken selectors and updating test data.
We'll analyze your current testing setup and show you exactly where autonomous testing would have the biggest impact.