Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Set up background task queues for AI agent workloads using Celery, RQ, and Dramatiq. Learn worker patterns, retry policies, and result backends for reliable agent job processing.
Master asyncio.Semaphore, token bucket, and sliding window rate limiters to control concurrent LLM API requests. Includes retry-after handling and adaptive throttling.
Build real-time streaming AI pipelines using Python async generators. Learn yield patterns, consumer chains, and backpressure for delivering LLM tokens to users instantly.
Master error handling in async Python for AI agents. Learn asyncio.timeout, task cancellation, cleanup patterns, and exception groups for robust agent systems.
Design and implement an async worker pool for processing concurrent AI agent sessions. Learn health monitoring, dynamic scaling, graceful drain, and production deployment patterns.
Learn how to implement the Router pattern for AI agents, using classification-based routing with confidence thresholds and fallback routes to direct requests to specialized handlers.
Master the Pipeline pattern for AI agents — design sequential processing stages with intermediate results, error propagation, and checkpointing for resilient multi-step workflows.
Implement the Map-Reduce pattern for AI agents to split large workloads across parallel agent workers and aggregate their results efficiently.
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