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 use the logit bias parameter to steer LLM outputs at the token level, suppress unwanted words, boost preferred vocabulary, and build more predictable agent behaviors.
Build AI agents that learn and adapt to individual user preferences over time — from implicit signal extraction and profile building to personalized responses — while respecting privacy boundaries.
Learn how to build knowledge graph memory for AI agents — extracting entities and relationships from text, storing them in graph structures, and querying connected information for richer agent reasoning.
Master caching strategies for AI agents — from response caching and embedding caching to tool result caching and smart invalidation — to reduce latency, cut API costs, and improve throughput.
Learn how to build distributed memory for multi-agent systems using Redis, shared databases, event sourcing, and consistency patterns — enabling multiple agent instances to collaborate with shared state.
Understand the different types of prompt injection attacks targeting AI agents, see real-world examples of direct and indirect injection, and learn why agent security must be a first-class engineering concern.
Build output scanning systems that detect PII leaks, toxic content, format violations, and off-topic responses before they reach your users, with practical Python implementations for each guardrail type.
Learn how to sandbox AI agent tool execution using Docker containers, restricted file systems, timeout enforcement, and resource limits to prevent agents from causing damage through code execution tools.
