Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
A hands-on guide to setting up Pinecone, creating serverless indexes, upserting embeddings, running similarity queries, and filtering results with metadata for production AI applications.
Master the Pipeline pattern for AI agents — design sequential processing stages with intermediate results, error propagation, and checkpointing for resilient multi-step workflows.
Engineer 99.9% uptime for an AI agent platform through redundancy design, health checking, circuit breakers, graceful degradation, and chaos engineering practices that find failures before your customers do.
Run effective post-incident reviews for AI agent failures using blameless retrospective techniques, structured PIR templates, timeline reconstruction, root cause analysis, and follow-up tracking to prevent recurring failures.
Learn how to validate AI agent quality after migrations and system changes. Covers validation checklists, regression testing, monitoring dashboards, and automated rollback triggers.
Build systematic post-mortem processes for AI agent failures including incident classification, automated root cause analysis, action item tracking, and a knowledge base that prevents recurring issues.
Master PostgreSQL JSONB for storing variable-structure agent outputs including tool call results, LLM responses, and agent metadata with proper indexing, partial updates, and query optimization.
Learn how to apply time series forecasting to AI agent data, predict conversation volume and cost trends, detect seasonality patterns, and build capacity planning models that keep your agents running smoothly.
Learn how to build AI agent pipelines with Prefect. Covers flow definition, task decorators, deployments, scheduling, and real-time monitoring for agent workloads.
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