LLM Function Calling in Production — Tool Design, Parallel Calls, and Error Recovery
Master function calling with schema design, parallel execution, error handling, and recursive loops to build autonomous LLM agents that work reliably at scale.
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Master function calling with schema design, parallel execution, error handling, and recursive loops to build autonomous LLM agents that work reliably at scale.
Learn how to integrate LLM calls into microservice architectures with async patterns, job queues, and service contracts that don''t introduce latency bottlenecks.
Route queries intelligently to cheaper or more capable models based on complexity, intent, and latency SLAs, saving 50%+ on LLM costs while maintaining quality.
Master end-to-end LLM observability with OpenTelemetry spans, Langfuse tracing, and token-level cost tracking to catch production issues before users do.
Implement comprehensive LLM observability with LangSmith/LangFuse integration, token tracking, latency monitoring, cost attribution, quality scoring, and degradation alerts.