openai-advanced-agents

Design and implement tool-using agents with OpenAI models.

Updated Jul 5, 2026

Search Tags:#architecture #llm #agent #evaluation #safety #openai #tool use

Ready to run

npx skills add https://github.com/ijindal1/jaunt --skill openai-advanced-agents

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: openai-advanced-agents
Source: https://github.com/ijindal1/jaunt/tree/main/.codex/skills/openai-advanced-agents
Command: npx skills add https://github.com/ijindal1/jaunt --skill openai-advanced-agents

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the design and implementation of advanced, reliable agents that can effectively use tools, manage state, and operate safely with OpenAI models.

Core Features & Use Cases

Agent Architecture: Design robust agent systems with clear separation of concerns (policy, planning, execution, verification).
Tool Design: Create well-defined, validated tools for agent interaction.
Control Loops: Implement reliable plan-act-check cycles for task execution.
Safety & Evaluation: Incorporate guardrails against prompt injection and establish evaluation harnesses.
Use Case: Develop a customer support agent that can access a knowledge base, execute diagnostic tools, and provide accurate, safe responses.

Quick Start

Use the openai-advanced-agents skill to design a customer support agent that can access a knowledge base and execute diagnostic tools.

Frequently Asked Questions about openai-advanced-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build reliable tool-using agents with OpenAI models?▼

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.

What is the best way to design agent architecture for multi-step workflows?▼

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.

How do I prevent prompt injection when building LLM agents?▼

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.

How do I set up an evaluation harness for OpenAI agents?▼

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.

Can I implement multi-agent handoffs for customer support 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.

Why do my LLM agents fail during multi-step task execution?▼

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.