Temporal Activity Retry Patterns for LLM API Calls: Backoff, Circuit Breaking, and Cost Caps
How to configure Temporal retry policies, circuit breakers, cost caps, and provider failover for LLM API calls in production workflows.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
How to configure Temporal retry policies, circuit breakers, cost caps, and provider failover for LLM API calls in production workflows.
A four-decision triage model for portfolio operators classifying AI initiatives by workflow evidence, ownership, data readiness, and maintenance burden.
Voice agents create business value when they leave behind useful artifacts: decisions, action items, open questions, evidence, handoffs, and review paths.
A decision framework for choosing between LangGraph and direct API calls — based on orchestration complexity, not ecosystem momentum.
One impressive voice-agent call is weak evidence. Production readiness requires repeatable scripted tests, boundary checks, artifact review, and cost controls.
How to design escalation hierarchies and HITL gates for CrewAI crews — when supervision adds safety vs when it adds friction and approval fatigue.
Voice agents earn trust when they know when not to speak. Silence policy turns restraint into an explicit design layer for real meetings.
How to build custom LangChain callback handlers with OpenTelemetry integration for vendor-independent observability — what to trace, how to structure it, and what it costs.
A voice agent that speaks still needs to listen. Duplex behavior, interruption policy, and yield rules decide whether the agent feels useful or intrusive.
Enterprise CrewAI deployments require auth integration, tenant isolation, and audit trails the framework does not provide. Here are the patterns that work in production.
Realtime voice agents receive partial transcripts, delayed intent, and ambiguous address signals. Treating fragments as finished commands creates brittle meeting behavior.
Three advanced LangGraph interrupt patterns — conditional approval, batch review, and timeout handling — with production Python implementations.