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AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents
arXiv – CS AI|Erik Pautsch, Tanmay Singla, Parv Kumar, Wenxin Jiang, Huiyun Peng, Behnaz Hassanshahi, Konstantin L\"aufer, George K. Thiruvathukal, James C. Davis||7 views
🤖AI Summary
Researchers propose AgentHub, a registry system for AI agents similar to software package repositories like npm or Hugging Face. The system aims to make AI agents discoverable, verifiable, and governable through structured manifests, evidence records, and lifecycle tracking.
Key Takeaways
- →AgentHub addresses the fragmented infrastructure for discovering and evaluating LLM-based agents.
- →The system provides structured contracts and evidence records to improve agent discovery beyond keyword searches.
- →Implementation includes publish-time validation, version-bound evidence, and append-only lifecycle logging.
- →Initial testing shows LLM-as-judge recommendation pipelines improve intent-accurate retrieval.
- →The registry aims to create standardized infrastructure for reliable, reusable agent ecosystems.
Read Original →via arXiv – CS AI
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