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 robust request validation for AI agent APIs using Pydantic v2 models, custom field validators, and discriminated unions. Learn how to handle nested agent configurations and return clear validation error responses.
Implement background processing for AI agent workloads using FastAPI BackgroundTasks, Celery integration, and custom task queues. Learn task status tracking, webhook notifications, and long-running agent job management.
Build a production middleware stack for AI agent APIs in FastAPI. Covers structured request logging, Bearer token authentication, sliding window rate limiting, and CORS configuration for agent frontends.
Handle file uploads in FastAPI for AI agent document processing and image analysis. Learn type validation, size limits, chunked uploads for large files, and async processing pipelines for uploaded content.
Write comprehensive tests for AI agent APIs using pytest and httpx. Covers TestClient usage, async test patterns, fixture design for database and LLM mocking, and strategies for testing streaming endpoints.
Deploy FastAPI AI agent backends to production with optimal Uvicorn and Gunicorn configuration, Docker multi-stage builds, health check endpoints, and graceful shutdown handling for long-running agent requests.
Break down the true cost of running AI agents in production, from token costs and tool invocations to infrastructure and storage. Learn to identify the biggest cost drivers and build a cost model for your agent systems.
Learn how to design a model routing system that sends simple queries to cheap models and escalates complex ones to powerful models. Reduce AI agent costs by 40-60% while maintaining quality with intelligent routing.
