Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·21h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
✦ Why It Matters
Engineers can leverage STAGE-Claw to create more effective benchmarks for evaluating personal agents in realistic environments.
Key Takeaways
- Utilize STAGE-Claw for creating realistic agent evaluation tasks.
- Focus on final system state correctness for performance measurement.
- Analyze task scores and tool-call reliability for insights.
- Explore the 40 challenging tasks developed in the benchmark.
How It Works
STAGE-Claw automates the generation of benchmark tasks by taking a task hint and creating a corresponding environment, task prompts, and ground truth. It validates these tasks to ensure they reflect realistic scenarios, allowing agents to be tested in conditions that mimic actual user interactions.
Performance is evaluated based on the correctness of the final state of the system, rather than just the textual output, providing a more comprehensive assessment of agent capabilities.