How do you implement AI agents for automated software testing?

AI testing agents autonomously generate, execute, and maintain test suites. Architecture: code analyzer (understands the codebase structure, dependencies, and change impact), test generator (creates test cases based on code analysis and requirements), test executor (runs tests and captures results), and reporter (analyzes failures and generates actionable reports). Test generation strategies: unit test generation from function signatures and docstrings, integration test generation from API schemas, end-to-end test generation from user stories, and property-based test generation from type definitions. Implementation: the agent reads source code, identifies untested paths using coverage analysis, generates test cases targeting those paths, executes them, and iterates on failures. For regression testing: the agent monitors code changes, determines affected test areas, generates targeted tests for changed functionality, and updates existing tests that break due to intentional changes. Tools: code parser (AST analysis), test runner (pytest, Jest, JUnit), coverage tool (Istanbul, coverage.py), and mutation testing (to verify test quality). Advanced capabilities: flaky test detection and remediation, test prioritization based on failure probability, and automatic test maintenance when APIs change. Quality metrics: code coverage achieved, mutation score, test stability, and defect detection rate.