Evaluating Enterprise-Grade AI Assistants: A Benchmark for Complex, Voice-Driven Workflows
As businesses increasingly integrate AI assistants, assessing their performance in real-world tasks—especially through voice interactions—is essential. Existing evaluation methods tend to focus on broad conversational skills or limited, task-specific tool usage, which do not adequately measure an AI agent’s ability to manage complex, specialized workflows across various domains. This gap emphasizes the need for comprehensive evaluation frameworks that address the challenges AI assistants face in practical enterprise settings, ensuring they effectively support intricate, voice-driven operations.
To tackle the limitations of existing benchmarks, Salesforce AI Research & Engineering developed a robust evaluation system tailored to assess AI agents in complex enterprise tasks across both text and voice interfaces. This internal tool supports the development of products like Agentforce and offers a standardized framework to evaluate AI assistant performance in four key business areas: managing healthcare appointments, handling financial transactions, processing inbound sales, and fulfilling e-commerce orders. The benchmark employs carefully curated, human-verified test cases to require agents to complete multi-step operations, utilize domain-specific tools, and adhere to strict security protocols across both communication modes.
Traditional AI benchmarks often focus on general knowledge or basic instructions; however, enterprise settings demand more advanced capabilities. AI agents in these contexts must integrate with multiple tools and systems, follow strict security and compliance procedures, and understand specialized terms and workflows. Voice-based interactions introduce additional complexity, particularly due to potential speech recognition and synthesis errors, especially in multi-step tasks. This benchmark guides AI development toward more dependable and effective assistants tailored for enterprise use.
Salesforce’s benchmark utilizes a modular framework with four key components: domain-specific environments, predefined tasks with clear goals, simulated interactions that reflect real-world conversations, and measurable performance metrics. It evaluates AI across four enterprise domains: healthcare appointment management, financial services, sales, and e-commerce. Tasks range from simple requests to complex operations involving conditional logic and multiple system calls. With human-verified test cases, the benchmark ensures realistic challenges that test an agent’s reasoning, precision, and tool handling in both text and voice interfaces.
The evaluation framework measures AI agent performance based on two main criteria: accuracy, which assesses how correctly the agent completes tasks, and efficiency, evaluated through conversational length and token usage. Both text and voice interactions are assessed, with the option to add audio noise to test system resilience. Implemented in Python, the modular benchmark supports realistic client-agent dialogues, multiple AI model providers, and configurable voice processing using built-in speech-to-text and text-to-speech components. An open-source release is planned, enabling developers to extend the framework to new use cases and communication formats.
Initial testing across top models like GPT-4 variants and Llama indicated that financial tasks were the most error-prone due to strict verification requirements. Voice-based tasks also experienced a 5–8% drop in performance compared to text. Accuracy declined further on multi-step tasks, particularly those requiring conditional logic. These findings underscore ongoing challenges in tool-use chaining, protocol compliance, and speech processing. While robust, the benchmark lacks personalization, diversity in real-world user behavior, and multilingual capabilities. Future work will address these gaps by expanding domains, introducing user modeling, and incorporating more subjective and cross-lingual evaluations.
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