Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Learn the three core strategies for managing conversation history in AI agents — sliding windows, summary-based compression, and compaction — to stay within context window limits while preserving critical information.
Learn how to measure AI agent conversation quality using automated scoring rubrics, LLM-as-judge patterns, and human correlation validation to ensure your agent produces coherent and helpful responses.
Build robust conversation repair strategies for AI agents including error detection, clarification prompts, rephrasing requests, and graceful recovery from misunderstandings.
Build conversation summarization systems that generate concise, actionable summaries of long AI agent interactions with key point extraction, decision tracking, and follow-up items.
Build a conversational booking agent that integrates with calendar APIs, handles timezone conversions, checks real-time availability, and manages confirmation and reminder flows.
Conversational AI transforms telecom with 99% of adopters reporting productivity gains. Learn how telecom companies deploy AI for service and operations.
Learn how coreference resolution enables AI agents to track pronouns and references across conversation turns, with practical implementations using spaCy, neural models, and LLM-based approaches.
Learn how Corrective RAG (CRAG) adds relevance scoring, re-retrieval, and web search fallback to catch and fix bad retrievals before they reach the user. Full Python implementation included.
Implement proven strategies to dramatically reduce AI agent operating costs through intelligent model routing, response caching, request batching, prompt optimization, and usage caps without sacrificing user experience.
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