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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Learn how to implement exact-match, semantic, and hybrid caching for AI agent responses. Achieve 30-60% cost reduction with proper cache architecture, hit rate optimization, and smart invalidation strategies.
Build a token budget management system with per-user quotas, per-request limits, enforcement middleware, and graceful degradation. Prevent cost overruns while maintaining service quality for your AI agents.
Optimize embedding costs for AI agent systems with practical strategies for caching embeddings, selecting cost-effective models, batch sizing, and storage optimization. Reduce embedding spend by 60-80%.
Optimize infrastructure costs for AI agent deployments with practical strategies for instance selection, auto-scaling, spot instances, and reserved capacity. Learn to match compute resources to actual workload patterns.
Build a comprehensive ROI framework for AI agent deployments. Learn to quantify business value, model costs accurately, track key metrics, and present ROI reports that justify continued investment in agent automation.
Build a production-ready cost dashboard for AI agents with real-time spend tracking, budget alerts, cost forecasting, and per-model breakdowns. Complete Python implementation with FastAPI and data aggregation.
Learn how to build an AI agent that helps shoppers discover products through natural language search, personalized filtering, side-by-side comparisons, and recommendation engines tailored to individual preferences.
Build an AI agent that handles the complete order support lifecycle — from tracking shipments and processing returns to managing exchanges and order modifications — reducing support ticket volume significantly.
