openai-advanced-agents

To build reliable tool-using agents, implement a clear architecture separating policy, planning, execution, and verification, and use validated tools with plan-act-check control loops to manage state and execute tasks safely.

Designing agent architecture for multi-step workflows requires separating concerns across policy, planning, execution, and verification, while incorporating memory and state management to maintain reliable plan-act-check cycles.

Preventing prompt injection in LLM agents involves incorporating specific safety guardrails and controls into the agent architecture, ensuring the system validates tools and restricts unauthorized actions during execution.

Setting up an evaluation harness for OpenAI agents requires establishing tracing and observability controls to monitor agent performance, verify tool calling accuracy, and assess reliability across multi-step workflows.

You can implement multi-agent handoffs for customer support workflows by designing agents that access a knowledge base, execute diagnostic tools, and safely hand off tasks while maintaining state and providing accurate responses.

LLM agents fail during multi-step task execution when they lack reliable control loops, proper memory management, and clear separation between planning and verification, leading to breakdowns in tool calling and state tracking.