Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 11·4 min read
Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Utilize the LLM as a software component for custom agents.
- Prototype with a general-purpose agent to kickstart development.
- Implement CLI for refined agent outputs.
- Conduct agent-to-agent testing for robust performance.
How It Works
The methodology consists of two preconditions: treating the LLM as a software component and employing building blocks like function calling and MCP. The process involves three iterative practices: first, prototype with a general-purpose agent; second, refine the prototype into a CLI; and third, test the agent using another agent to simulate real-world scenarios.
This cycle allows for continuous improvement and adaptation of the agent.