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 production-ready Docker images for AI agents using multi-stage builds, proper dependency management, non-root users, and environment variable configuration for reproducible deployments.
Deploy and scale AI agent services on Kubernetes with Deployments, Services, Horizontal Pod Autoscalers, resource limits, and health checks for production-grade reliability.
Understand the ReAct (Reasoning + Acting) pattern that powers most modern AI agents, with a step-by-step breakdown of observation loops and a working Python implementation.
A deep dive into the agent loop — the fundamental control flow that powers every AI agent. Learn loop mechanics, termination conditions, maximum iteration strategies, and how to prevent infinite loops.
Build secure file system tools that let AI agents read, write, list, and manage files within a sandboxed directory. Covers path validation, sandboxing strategies, permissions, and safety considerations for production deployment.
Learn how AI agents select the right tool from a large toolset. Covers tool routing strategies, writing descriptions that guide selection, handling the too-many-tools problem, and building intelligent tool dispatchers.
Build real-time AI agent interfaces using WebSocket connections in FastAPI with connection lifecycle management, heartbeat mechanisms, and structured message protocols.
Implement Server-Sent Events (SSE) to stream AI agent responses token by token to browser clients using FastAPI StreamingResponse, EventSource API, and proper reconnection handling.
