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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Implement production-grade retry logic for LLM API calls using exponential backoff, jitter, and the Tenacity library. Learn when to retry, when to stop, and how to avoid the thundering herd problem.
Build automatic failover systems that seamlessly switch between LLM providers when your primary model is unavailable. Learn provider health checks, quality comparison, and cost-aware routing.
Design AI agent systems that maintain useful service even when critical components fail. Learn degradation levels, feature flags, reduced-functionality modes, and transparent user communication strategies.
Implement comprehensive timeout strategies for AI agent pipelines including cascading timeouts, deadline propagation, and proper cleanup of abandoned requests to prevent resource leaks.
Implement idempotency patterns for AI agent tool calls to ensure retries never cause duplicate bookings, double charges, or repeated notifications. Covers idempotency keys, state checking, and tool-level design.
Build comprehensive health monitoring for AI agent systems that checks LLM providers, databases, and tool integrations. Learn health check patterns, dependency graphs, degraded state detection, and alerting.
Build AI agents that detect their own errors, apply correction strategies, and learn from failures through feedback loops. Covers error detection, self-correction, escalation paths, and continuous improvement.
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.
