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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Build automated CI/CD pipelines for AI agent services using GitHub Actions with prompt regression testing, integration tests, Docker image builds, and canary deployment strategies.
Implement blue-green deployment strategies for AI agent services to achieve zero-downtime updates, safe model swaps, traffic splitting, and instant rollback for prompt and model changes.
Deploy AI agents as serverless functions on AWS Lambda and Google Cloud Functions with cold start optimization, timeout handling, stateless architecture, and cost-effective scaling strategies.
Learn how to instrument AI agent systems with OpenTelemetry for end-to-end distributed tracing, including span creation, custom attributes for LLM calls, and trace context propagation across multi-agent pipelines.
Build production-grade Grafana dashboards for AI agent systems that visualize conversation throughput, per-model costs, LLM latency percentiles, and tool usage patterns using Prometheus metrics.
Implement structured logging for AI agent systems with correlation IDs, log levels, sensitive data redaction, and queryable JSON output that makes debugging production agent issues fast and audit-ready.
Build a complete cost tracking system for AI agents that attributes token usage to individual users and features, sets budget alerts, and provides dashboards for controlling LLM spend in production.
Learn systematic approaches to profile AI agent latency, identify bottlenecks in multi-step pipelines, and apply targeted optimizations using timing decorators, flame charts, and parallel execution patterns.
