Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Learn how to build an end-to-end analytics pipeline for AI agents, from event collection and schema design to data warehousing, ETL processing, and query patterns that surface actionable insights.
Learn how to define conversation funnels for AI agents, track user journeys through interaction stages, identify drop-off points, and optimize conversion rates with data-driven insights.
Learn how to track token consumption across AI agents, attribute costs to specific features and users, identify usage trends, and implement optimization strategies that reduce LLM spend without sacrificing quality.
Learn how to define, calculate, and benchmark the core effectiveness metrics for AI agents including resolution rate, containment rate, first-contact resolution, and strategies for systematic improvement.
Learn how to define user cohorts for AI agent interactions, perform retention analysis, cluster users by behavior patterns, and use cohort insights to personalize agent responses and improve outcomes.
Learn how to set up Grafana dashboards for AI agent monitoring, configure data sources, design effective panels for latency, throughput, and error rates, and create alert rules that catch problems before users notice.
Learn how to build a comprehensive ROI model for AI agent deployments, including cost modeling, savings calculation, productivity gains, and a practical formula that quantifies business value for stakeholders.
Learn how to mine AI agent conversation logs for actionable patterns using text mining, topic modeling, pattern extraction, and automated insight generation that drives agent improvement.
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