The Open Agent Leaderboard
The Open Agent Leaderboard has been launched to evaluate AI agents based on their full system performance rather than just the underlying models. This new benchmark assesses agents across various tasks and reports both their quality and cost, providing insights into their generality. The initiative aims to foster a better understanding of how well AI agents can adapt to diverse settings without extensive customization.
- ▪The leaderboard measures the performance of full agent systems, not just the models they use.
- ▪It includes six benchmarks that test different types of realistic tasks like coding and customer service.
- ▪The evaluation framework aims to provide a clearer picture of an agent's generality and deployment worthiness.
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| Original publisher | Hugging Face Blog |
| Canonical URL | https://huggingface.co/blog/ibm-research/open-agent-leaderboard |
| Publication time | Mon, 18 May 2026 14:12:58 GMT |
| Retrieval time | 2026-05-18T14:14:56.649Z |
| Last seen | 2026-05-18T14:14:56.649Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 1rTJmUQk_Vq5 |
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| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to Articles The Open Agent Leaderboard Enterprise Article Published May 18, 2026 Upvote 1 Elron Bandel Elron Follow ibm-research Can we measure generality? What we built How to read the leaderboard What we're already learning What's public today What we want from the community What's next Closing Related reading How good are general purpose AI agents? We built an open evaluation framework to find out. Most evaluations in AI report a simple result: what score each model got on which benchmarking task. When you deploy an agent, you're not just choosing a model. You're choosing a full system: what tools the agent can use, how it plans its steps, what it remembers between actions, how it recovers when something goes wrong.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hugging Face Blog.
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