Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Learn how to build defensible network effects around an AI agent platform through developer relations, partner programs, content strategy, and community flywheel design. Covers ecosystem metrics, incentive structures, and growth loops.
Learn how to design, label, and maintain evaluation datasets for AI agents, covering dataset structure, diversity requirements, edge cases, and ongoing maintenance strategies.
Understand why GPUs dominate AI workloads, how their massively parallel architecture maps to neural network math, and what high-bandwidth memory means for model training and inference.
Build an AI agent that classifies home service requests, detects emergencies, provides preliminary quotes, and schedules technician visits for plumbing, HVAC, and electrical businesses.
Build generic API integration tools that let AI agents call REST and GraphQL endpoints with proper authentication, error handling, retry logic, and response mapping. Learn patterns for building flexible yet safe API tools.
Implement the Chain of Responsibility pattern for AI agents with fallback chains, capability matching, and cost-optimized ordering to handle requests efficiently.
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.
Understand the difference between bi-encoders and cross-encoders, then build a re-ranking pipeline that dramatically improves search precision by scoring query-document pairs jointly rather than independently.
Build a document analysis agent that uploads PDFs to Claude, performs page-level analysis, extracts tables and structured data, and compares information across multiple documents.
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