OpenAI Agents SDK

Explore how AI agents autonomously reason, plan, and take actions to accomplish goals.

Delegation, routing, and specialist-agent handoffs that transfer control and context between agents in a multi-agent system.

Approval workflows, interrupt/resume patterns, and human review gates that keep production AI agents safe and controllable.

Safety constraints and validation on AI inputs and outputs — content filters, schema checks, and policy enforcement in agent and app pipelines.

Understand how LLMs invoke external tools and APIs—including computer use and browser use—to extend capabilities beyond text generation.

Understand the paradigm shift from prompt-response AI to systems that act autonomously.

Learn how multiple AI agents collaborate, delegate, and coordinate to solve complex problems.

ReAct, Plan-and-Execute, reflexion, and other architectural patterns for building reliable AI agents.

How AI agents decompose goals into steps, create plans, and adapt when things go wrong.

Short-term, long-term, and episodic memory patterns for AI agents that need to remember across sessions.

RAG systems where agents decide what to retrieve, when to search, and how to synthesize multi-step answers.

When to use deterministic LLM workflows vs autonomous agents - and why most production systems are hybrids.

Checkpointing, persistence, retries, and resumable workflows for long-running AI agents that survive failures and interruptions.

Engineer typed LLM tool intents with schema validation, authorization, timeouts, idempotency, observability, and safe production execution.