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CRM-Agent-Bench tests tool-using agents on 555 CRM tasks

Original title: CRM-Agent-Bench: Benchmarking Tool-Using Agents in Stateful Enterprise Environments

Hugging Face daily papersby Monojit Banerjee, Zhujun Lan, Defu Cao ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

Sixteen systems score 64.32% to 83.60% strict success, with leaders diverging sharply in cost and domain reliability.

CRM-Agent-Bench is an execution-grounded benchmark of 555 stateful, multi-turn customer-relationship tasks across nine business domains and 36 capability tags. Success is the strict conjunction of task-specific checks over actions, arguments, order, response, target state, and protected state. Sixteen systems on the audited final release are scored for coverage, latency, and cost.

Key facts
  • Each task uses an executable contract drawn from those declared checks.
  • Leading systems cluster in success but differ in cost and per-domain reliability.
  • The benchmark structure is described as portable for later model generations.
  • Code is published at github.com/salesforce-misc/crm-agent-bench.
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From the original article

1 min read at huggingface.co

Deploying language agents in real applications demands more than the right answer: an agent must execute a multi-step business process correctly, follow domain-specific rules, and interact with a user across a dialogue, where the gap between saying and doing is the gap between a satisfied customer and a real loss.

Hugging Face daily papers

By Monojit Banerjee, Zhujun Lan, Defu Cao · huggingface.co

Read the full article at Hugging Face daily papers

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