Conversational AI projects combine builder platforms, runtime orchestration, analytics, and human-centered testing — and each stage has specific testing needs. This collection compares tools that address those needs so teams can design realistic test plans and observability pipelines for chat and voice agents. No-code/low-code builders speed prototyping and create predictable execution paths that are easy to fuzz and regression-test. Enterprise agent platforms add governance, RBAC, memory and RAG controls that matter for compliance and reproducibility. Dedicated conversational analytics and evaluation services surface signals about failure modes, hallucinations, user frustration, and content-policy violations, enabling automated evaluation and alerting. Voice-specific systems require extra validation for ASR/NLU quality, prosody behavior, fraud detection and moderation under real-time constraints. Finally, user testing and UX research close the loop by validating end-to-end experience and guiding test-case prioritization. Practical use cases covered include: automated scenario-based testing for multi-step workflows, continuous regression tests for RAG/memory changes, monitoring and anomaly detection on production conversations, voice moderation and fraud detection for telephony apps, and rapid UX validation with real user sessions. The tools selected here span builder + test features (visual editing, QA agents), observability and analytics for conversational signals, voice-intelligence modules, and platforms to operationalize governance and automated model evaluation. Use this comparison to pick a combination of building, testing, and monitoring tools that match your channel mix (chat, voice, contact center), compliance needs, and operational maturity rather than chasing a single “all-in-one” promise.