Salesforce Launches Benchmark to Test AI Voice Assistants on Complex Enterprise Workflows

As organizations bring AI assistants into more operations, it’s critical to check how well these systems execute real tasks, especially through voice interactions. Current evaluation approaches target general conversational abilities or narrow, task-focused tool use. These tests miss how well an AI handles detailed, domain-specific processes across fields. That shortfall has led to calls for more complete assessment models that match the demands of enterprise settings and verify that voice-driven assistants can support complex workflows in real scenarios.

To fill this gap, Salesforce AI Research & Engineering created a testing platform to rate AI agents on enterprise tasks via text and speech. This in-house tool underpins offerings such as Agentforce. It provides a uniform method to assess performance in four key areas: scheduling healthcare visits, performing financial operations, managing incoming sales inquiries, and processing e-commerce orders. Test cases include tasks like booking follow-up appointments, calculating loan repayments, qualifying leads through Q&A scripts, and coordinating shipping across multiple warehouses, reflecting realistic pressure points developers face. By drawing on human-reviewed scenarios, the evaluation requires agents to finish multi-step actions, work with domain tools, and follow strict security rules in both communication methods.

Typical AI tests focus on general knowledge or simple directives, but enterprise scenarios call for advanced skill sets. Agents must link into various business systems, meet compliance standards, and handle specialized terminology and workflows. Speech-based exchanges add extra complexity through possible misrecognition or synthesis faults, especially across multiple steps. This benchmark steers development toward more reliable assistants designed for business use.

The benchmark uses a modular design made up of four parts: domain-specific settings, clearly defined tasks, simulated dialogues that mimic actual conversations, and quantifiable performance indicators. It spans four domains: appointment management for healthcare, core financial services, sales processing, and online retail fulfillment. Tasks range from single commands to complex flows involving conditional logic and several system calls. Human-validated cases ensure realistic tests of an agent’s reasoning, precision, and ability to handle tools in both text and voice channels.

Performance metrics cover two dimensions: accuracy, which measures how correctly the agent completes each task; and efficiency, gauged by dialogue length and token usage. Both speech and text modes are evaluated, with an option to introduce audio noise layers to test robustness. The system logs detailed timestamps, error types, and tool invocation patterns, generating dashboards that highlight bottlenecks and help teams fine-tune model responses. Built in Python, the toolkit supports true-to-life client-agent exchanges, connections to multiple AI providers, and configurable speech-to-text and text-to-speech modules. An open-source release is planned to let developers add new use cases and communication styles.

Initial trials of leading models such as GPT-4 variants and Llama revealed that financial workflows produced the most errors owing to strict verification needs. Speech tests lagged by five to eight percent relative to text interactions. Accuracy dipped further on multi-step flows, especially those that rely on conditional branches. Model versions with larger parameter sets showed slightly better comprehension, though they struggled on conditional flows requiring nested validations. These results point to persistent challenges in tool chaining, protocol compliance, and speech handling.

Next steps include adding personalization layers, a broader array of user profiles, and support for multiple languages. Plans call for more subjective evaluations and cross-language trials to capture the variety of real-world interactions and user experiences that go beyond strict performance metrics. The team also plans to introduce varied speaker accents and noise profiles to more accurately simulate customer environments.

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