LiveAgentBench: Comprehensive Benchmarking of Agentic Systems Across 104 Real-World Challenges

Researchers have released LiveAgentBench, a comprehensive benchmark featuring 104 real-world scenarios to evaluate AI agent performance across practical applications. The benchmark uses a novel Social Perception-Driven Data Generation method to ensure tasks reflect actual user requirements and includes 374 total tasks for testing various AI models and frameworks.

• →LiveAgentBench addresses limitations in existing AI agent benchmarks by using real-world user tasks sourced from social media and products.
• →The benchmark includes 104 scenarios with 374 total tasks, split between validation and testing sets.
• →A novel Social Perception-Driven Data Generation method ensures task relevance, complexity, and verifiability.
• →The benchmark enables evaluation of various AI models, frameworks, and commercial products to identify performance gaps.
• →The system allows for continuous updates with fresh queries from real-world interactions.