Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Master the techniques for managing conversation context in chat agents, including context window optimization, message pruning strategies, summarization, and topic tracking for coherent multi-turn interactions.
Implement fan-out fan-in patterns for AI agents to distribute work across parallel sub-tasks, aggregate results, handle partial failures gracefully, and enforce timeouts on straggler tasks.
Build an AI agent that automates patient intake by generating dynamic forms, verifying insurance eligibility, collecting medical history, and managing consent documents before the visit.
Build a feedback loop pipeline that collects user signals, categorizes feedback, analyzes failure patterns, and automatically updates prompts and retrieval to improve AI agent performance over time.
Learn how to load test AI agent systems by simulating thousands of concurrent conversations, collecting meaningful metrics, identifying bottlenecks, and building capacity planning models that predict scaling needs.
Learn how to build an AI agent for car dealerships that searches vehicle inventory, schedules test drives, and generates finance quotes using tool-calling patterns and structured vehicle databases.
Master techniques for debugging multi-agent systems including interaction diagrams, distributed message tracing, replay tools, and correlation analysis. Turn opaque agent failures into diagnosable problems.
Implement progressive disclosure patterns in AI agent conversations to manage information overload, layer detail levels, design expand/collapse interactions, and craft effective follow-up prompts.
Learn how to leverage prompt caching features from OpenAI and Anthropic to dramatically reduce latency and cost by reusing cached prompt prefixes across requests.
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