Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Discover how to identify and fix excessive token consumption in AI agents by analyzing prompt bloat, conversation history growth, tool definition overhead, and applying targeted optimization strategies.
Learn how to diagnose and fix common streaming problems in AI agents including dropped tokens, connection resets, partial responses, and timeout failures with practical debugging techniques.
Master techniques for diagnosing and fixing context loss during multi-agent handoffs, including context inspection, handoff logging, serialization validation, and state verification strategies.
Learn systematic approaches to debugging RAG retrieval failures including query analysis, embedding inspection, relevance scoring evaluation, and chunk quality review for more accurate AI agent responses.
A practical guide to diagnosing and fixing voice AI agent issues including audio quality degradation, speech-to-text transcription errors, text-to-speech artifacts, and end-to-end pipeline latency.
Build a production observability stack for AI agents with structured logging, distributed trace correlation, timeline reconstruction, and systematic root cause identification techniques.
Learn how to build a comprehensive debug mode for AI agents with toggle-able verbose logging, step-through execution callbacks, state dumps, and conversation replay capability for efficient troubleshooting.
Learn how to use the Claude Message Batches API to process thousands of agent tasks asynchronously with 50% cost reduction, including job monitoring, result processing, and error handling.
