arXiv:2606.13608v1 Announce Type: new Abstract: Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, create test-production mismatch, and limit fair comparison across diverse agent designs. The root problem is the lack of an open, agent-agnostic assessment interface. We advocate Agentified Agent Assessment (AAA), where evaluation is performed by judge agents and all participants interact through standardized protocols: A2A for task management and MCP for tool access. Con
The rapid advancement of agent systems necessitates standardized evaluation methods, as current benchmarks are proving inadequate for diverse agent designs and practical deployment.
Standardized, open, and reproducible agent assessment is critical for the responsible development, deployment, and fair comparison of AI agents across industries, impacting future AI capabilities and adoption.
The proposed 'Agentified Agent Assessment' (AAA) framework, utilizing judge agents and standardized protocols, fundamentally shifts how autonomous AI agents will be evaluated, moving beyond current LLM-centric, fragmented methods.
- · AI agent developers
- · Enterprises adopting AI agents
- · AI governance bodies
- · Research institutions
- · Proprietary benchmark providers
- · AI agents with undisclosed evaluation methodologies
The adoption of A2A and MCP protocols could accelerate the development and interoperability of advanced AI agents.
Improved evaluation standards could lead to faster commercialization and more reliable integration of agentic systems into critical infrastructure.
A highly standardized evaluation ecosystem might accelerate the 'collapse' of white-collar workflows and SaaS layers, if agents can be deployed with greater confidence in their performance and safety.
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Read at arXiv cs.AI