General Agent Evaluation
#General Agent Evaluation #AI benchmarks #Unified Protocol #Exgentic framework #Open General Agent Leaderboard #Domain-specific agents #AI research #arXiv
📌 Key Takeaways
- Researchers published a comprehensive framework for evaluating general-purpose AI agents
- Existing benchmarks are unsuitable for evaluating general agents as they assume domain-specific integration
- The team created the first Open General Agent Leaderboard benchmarking five agents across six environments
- General agents can perform comparably to specialized ones without environment-specific tuning
📖 Full Retelling
A team of researchers led by Elron Bandel and including 14 co-authors from the field of Artificial Intelligence published a groundbreaking paper titled 'General Agent Evaluation' on February 26, 2026, addressing the critical need for systematic evaluation of general-purpose AI agents that can perform tasks across diverse environments without domain-specific engineering. The research highlights that while the promise of general-purpose agents remains largely unrealized, current AI systems are predominantly specialized, with no comprehensive framework available to evaluate their general performance capabilities. The paper introduces conceptual principles for evaluating such agents, a Unified Protocol enabling agent-benchmark integration, and Exgentic - a practical framework specifically designed for general agent evaluation. In their comprehensive study, the researchers benchmarked five prominent agent implementations across six different environments, creating the first-ever Open General Agent Leaderboard to systematically compare and evaluate these systems. Their experiments revealed that general agents can effectively generalize across diverse environments, achieving performance levels comparable to domain-specific agents without requiring any environment-specific tuning, marking a significant advancement in the field of artificial intelligence research.
🏷️ Themes
Artificial Intelligence, Evaluation Frameworks, General-Purpose Systems
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
UniPro
High-speed interface technology
UniPro (or Unified Protocol) is a high-speed interface technology for interconnecting integrated circuits in mobile and mobile-influenced electronics. The various versions of the UniPro protocol are created within the MIPI Alliance (Mobile Industry Processor Interface Alliance), an organization that...
Entity Intersection Graph
Connections for Large language model:
🌐
Educational technology
4 shared
🌐
Reinforcement learning
3 shared
🌐
Machine learning
2 shared
🌐
Artificial intelligence
2 shared
🌐
Benchmark
2 shared
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.22953 [Submitted on 26 Feb 2026] Title: General Agent Evaluation Authors: Elron Bandel , Asaf Yehudai , Lilach Eden , Yehoshua Sagron , Yotam Perlitz , Elad Venezian , Natalia Razinkov , Natan Ergas , Shlomit Shachor Ifergan , Segev Shlomov , Michal Jacovi , Leshem Choshen , Liat Ein-Dor , Yoav Katz , Michal Shmueli-Scheuer View a PDF of the paper titled General Agent Evaluation, by Elron Bandel and 14 other authors View PDF HTML Abstract: The promise of general-purpose agents - systems that perform tasks in unfamiliar environments without domain-specific engineering - remains largely unrealized. Existing agents are predominantly specialized, and while emerging implementations like OpenAI SDK Agent and Claude Code hint at broader capabilities, no systematic evaluation of their general performance has been pursued. Current agentic benchmarks assume domain-specific integration, encoding task information in ways that preclude fair evaluation of general agents. This paper frames general-agent evaluation as a first-class research objective. We propose conceptual principles for such evaluation, a Unified Protocol enabling agent-benchmark integration, and Exgentic - a practical framework for general agent evaluation. We benchmark five prominent agent implementations across six environments as the first Open General Agent Leaderboard. Our experiments show that general agents generalize across diverse environments, achieving performance comparable to domain-specific agents without any environment-specific tuning. We release our evaluation protocol, framework, and leaderboard to establish a foundation for systematic research on general-purpose agents. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22953 [cs.AI] (or arXiv:2602.22953v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.22953 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Elron B...
Read full article at source