E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios
Researchers have introduced E-Bench, a synthetic benchmark designed to evaluate how AI agents handle complex, multi-step tasks within real-world product environments.
- E-Bench focuses on multi-step tool use in stateful environments.
- The benchmark includes 323 tasks across gaming, music, and meeting software domains.
- It moves evaluation beyond simple, isolated API calls to complex, multi-step trajectories.
Current evaluations for Large Language Models often fail to capture the complexity of real-world agentic behavior. Most benchmarks focus on isolated API calls or short sequences, which do not reflect how agents actually operate in production environments where they must manage state and navigate complex workflows.
E-Bench addresses this gap by providing a synthetic benchmark consisting of 323 state-changing tasks. These tasks are set within three distinct product domains: Honor of Kings, QQ Music, and Tencent Meeting. This allows researchers to test how agents gather hidden information, compose tool calls, and execute changes that persist across multiple steps.
By simulating these realistic product scenarios, E-Bench provides a more rigorous framework for measuring the reliability and reasoning capabilities of autonomous AI agents.
Provides a more realistic testing ground for building autonomous agents.
Highlights the shift from simple LLM prompting to complex agentic workflows.
- Stateful environment
- An environment where actions taken by an agent change the current status or context, affecting future interactions.
- Multi-step tool use
- The ability of an AI to use multiple functions or APIs in a sequence to achieve a complex goal.
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