Benchmarks are Not Enough: RAMP for Runtime Assessing of Agentic Models in Production Systems
arXiv:2605.27492v1 Announce Type: cross Abstract: LLM agents are rapidly evolving from coding assistants into autonomous software engineering systems. However, existing evaluation methodologies remain largely centered on static, isolated, and short-horizon benchmarks that fail to capture the dynamic complexity of real-world production workflows. As a result, benchmark performance may poorly reflect practical capability under realistic runtime environments involving long execution chains, tool interactions, dependency management, and iterative feedback loops. We thus present RAMP, a production-
As LLM agents move from research to production, the limitations of current static evaluation methods become critically apparent, necessitating new runtime assessment tools.
The effectiveness of AI agents in real-world applications hinges on accurate evaluation of their performance and reliability, directly impacting their adoption and the scaling of agentic workflows.
The focus for evaluating AI agents is shifting from theoretical benchmarks to practical, runtime assessments within complex production environments, which could accelerate robust, deployable agent development.
- · AI Agent Developers
- · Enterprises Adopting AI Agents
- · Software Testing Frameworks
- · Developers Relying Solely on Static Benchmarks
- · Inflexible Software Development Methodologies
More reliable and capable AI agents will be deployed in production systems.
This will accelerate the automation of white-collar workflows and increase operational efficiency across industries.
The enhanced performance and trust in AI agents could lead to a rapid expansion of agentic systems, potentially reshaping entire sectors and driving demand for new compute infrastructure.
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Read at arXiv cs.AI