Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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A new architectural pattern combining retrieval-augmented generation with autonomous agents becomes the standard for enterprise knowledge work and information retrieval.
Master the art of formatting tool results so LLMs can effectively parse and reason about them. Covers string formatting strategies, truncation, structured vs unstructured results, error messages, and token-efficient output design.
Learn a practical decision framework for choosing between prompt engineering, retrieval-augmented generation, and fine-tuning based on cost, data requirements, latency, and use case complexity.
Build AI agents that learn and adapt to individual user preferences over time — from implicit signal extraction and profile building to personalized responses — while respecting privacy boundaries.
Create an AI agent that aggregates tracking data from multiple carriers, predicts ETAs, handles delivery exceptions, and provides real-time package status through a conversational interface.
G.722 fixed 64 kbps with sub-2 ms latency or Opus 6-510 kbps with adaptive bitrate? For voice AI agents the answer depends on whether your media path crosses a SIP trunk or stays end-to-end WebRTC.
Design multi-agent systems that span organizational boundaries with proper API contracts, trust boundaries, data sharing controls, and compliance frameworks. Build federated agent architectures safely.
Red teaming AI models through adversarial testing, LLM vulnerability scanning, and prompt injection defense is essential before deployment. A complete guide to AI security testing.
McKinsey's annual AI survey reveals dramatic enterprise adoption acceleration, with customer service and sales leading use cases and average ROI reaching 340% for mature deployments.
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