arXiv:2606.05670v1 Announce Type: new Abstract: Does adding more agents help an LLM workflow once compared systems share the same benchmark loader, tool access, answer contract, usage accounting, and trajectory logging? We introduce BenchAgent, an evaluation framework that places single-agent, fixed multi-agent (MAS), and evolving MAS workflows under one normalized execution and logging protocol. BenchAgent evaluates these substrate-internal workflows across ten reasoning, coding, and tool-use benchmarks with GPT-4.1, and separately reports a Protocol-Aligned External (PAE) GAIA study of a run
The proliferation of LLM-based agentic systems necessitates robust evaluation frameworks to understand their emergent capabilities and optimal configurations.
This research provides a standardized method for evaluating LLM agent workflows, crucial for industrial deployment and identifying effective multi-agent architectures.
The ability to systematically compare, validate, and optimize single-agent versus multi-agent LLM systems across various benchmarks moves from anecdotal to protocol-aligned evaluation.
- · AI Agent developers
- · Enterprises adopting AI workflows
- · Benchmark creators
- · Inefficient multi-agent architectures
- · Ad-hoc AI workflow deployments
Improved performance and reliability of LLM agent systems lead to faster adoption in complex tasks.
An optimized understanding of multi-agent collaboration could accelerate the collapse of certain white-collar workflows, including design, coding, and strategic analysis.
The demonstrated superiority of multi-agent systems might drive investments towards developing more sophisticated agent orchestration layers and foundational models optimized for multi-agent interaction.
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Read at arXiv cs.AI