Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·19h ago
AI Research
arXiv CS.AI·May 28·8 min read
Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems
TL;DR
Benchmarks alone fail to catch real-world failures in agentic AI systems (autonomous agents that take actions in production). RAMP (Runtime Assessing of Agentic Models in Production) was built to monitor agent behavior during live operation, detecting issues benchmarks miss.
✦ Why It Matters
Engineers can now detect and fix agentic AI failures in production before they harm users, rather than discovering problems only through benchmarks.
Key Takeaways
- Implement RAMP for realistic evaluation of AI models in production.
- Recognize the limitations of traditional benchmarks in dynamic environments.
- Monitor task completion rates to identify model performance issues.
- Evaluate resource efficiency to optimize computational costs.
How It Works
RAMP integrates a unified runtime assessment architecture that standardizes orchestration and execution interfaces. It simulates complex software engineering tasks, including compiler-construction workloads with serial dependencies, allowing for a realistic evaluation of model performance under production-like conditions.