Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Build automated quality monitoring for LLM responses in production that detects quality degradation using scoring pipelines, drift detection, and alerting before users are impacted at scale.
Build conversation analytics for AI agents that measure success rates, identify drop-off points, track user satisfaction, and surface patterns that drive product and prompt improvements.
Implement rigorous A/B testing for AI agents to compare prompts, models, and configurations in production with proper experiment design, traffic splitting, statistical significance, and safe rollout strategies.
Build compliance-grade audit logging for AI agents operating in regulated industries, covering immutable log storage, data retention policies, SOC2 and HIPAA requirements, and log archival strategies.
Learn how to mock LLM API calls in your AI agent tests using FakeLLM objects, response fixtures, and assertion patterns for fast, deterministic, cost-free unit tests.
Learn how to structure integration tests for AI agent pipelines that make real LLM calls, manage API costs, use snapshot testing, and run safely in CI/CD.
Learn how to design, label, and maintain evaluation datasets for AI agents, covering dataset structure, diversity requirements, edge cases, and ongoing maintenance strategies.
Learn how to use LLMs as automated judges to evaluate AI agent responses with scoring rubrics, calibration techniques, and multi-criteria evaluation frameworks in Python.
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