AI-native engineering teams need AI-native quality engineering. Each agent runs on a curated library of named skills — IDE-side codegen, MCP-connected integrations, analysis, and reporting. Try them live.
TARA authors the spec. EVAN compiles automation. LEO and SARA exercise the system in parallel — performance and security. When the target has an LLM component, NORA runs alongside them and audits the LLM across five dimensions — adversarial, hallucination, instruction-following, bias, toxicity. ARIA reads every signal and produces a sprint quality report — pushed back to your tools via MCP.
Translates ambiguous requirements into structured, executable test specifications.
Converts test specifications into production-grade automation code.
Stresses systems under realistic load and surfaces what breaks before customers do.
Probes APIs and applications for OWASP-class vulnerabilities and risk exposure.
Audits your LLM features end-to-end — adversarial red-team (OWASP LLM Top 10), factual hallucination, instruction-following, demographic bias, toxicity + safety. Produces a severity-scored eval report with enforceable policies for the Guardrails layer.
Synthesizes signal from every test run into decisions leadership can act on.