CoTester operates as an AI agent that ingests product context from your existing QA workflows and documentation. The system pulls specifications directly from JIRA stories and converts them into executable test logic within minutes. It's built for enterprise teams managing complex test scenarios across multiple environments.
The core architecture uses a multi-modal Vision Language Model that processes visual elements, text content, and layout simultaneously. This VLM interprets application screens the way a human tester would, identifying UI components through their visual appearance rather than relying solely on DOM selectors. When you feed it a JIRA story, the agent parses requirements and generates test code automatically.
Tests run on real browsers, not simulated environments. The AgentRx engine monitors execution in real time and handles UI changes on the fly. It'll detect structural shifts and even complete redesigns mid-execution, adjusting selectors and navigation paths without failing the test. That's different from typical self-healing, which usually just updates a broken selector.
The system includes validation checkpoints where it pauses before critical actions. Your team can review the agent's intended direction and approve or redirect. It learns from each interaction and feedback cycle, reducing test flakiness over time.
Bug identification happens automatically during runs. The agent logs issues it encounters without manual intervention. You can schedule executions for nightly builds, weekly regression cycles, or pre-release validation.
JIRA integration is native, pulling stories and pushing results back. CI/CD pipeline integration lets you trigger tests from your existing deployment workflows.
A free trial exists. Cost information isn't available on their public website.
The system demands enterprise-scale infrastructure to support the VLM processing and real browser execution across multiple test environments simultaneously.
