Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Anthropic's MCP protocol gains universal adoption with support from OpenAI, Google, Microsoft, and 500+ tool connectors, establishing the standard for how AI agents interact with external systems.
Design and implement a model router that classifies query complexity and directs agent requests to the most cost-effective model. Build fallback chains, measure routing accuracy, and optimize per-query costs.
Build a behavioral monitoring system for AI agents that establishes baselines, detects anomalies in tool usage and output patterns, triggers alerts, and implements automated shutdown for runaway agents.
Build a monitoring system for AI agent data pipelines that tracks ingestion metrics, detects data drift, alerts on failures, and enforces SLAs to keep your agent's knowledge base fresh and reliable.
Learn how to evaluate multi-agent AI systems by measuring handoff accuracy, information retention across agents, routing correctness, and end-to-end coordination quality.
Architect a single chat agent that operates across web widgets, WhatsApp, Slack, and SMS by building a channel abstraction layer, message normalization pipeline, and channel-specific rendering adapters.
Manage AI agent configurations across multiple Kubernetes clusters using GitOps workflows, config synchronization, drift detection, and environment promotion pipelines.
Learn how to build a multi-index RAG system that routes queries to appropriate collections, merges results, and normalizes relevance scores across heterogeneous document stores.
Learn how to detect and handle multiple intents in a single user message, including intent splitting, parallel processing, and delivering coherent ordered responses.
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