Enterprise AI Analysis
MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations
Conversational agents frequently encounter ambiguous user requests, necessitating effective clarification for successful task completion. While multi-agent architectures are gaining traction for managing complex conversational scenarios, ambiguity resolution remains a critical and underexplored challenge, particularly in determining which agent should initiate clarification and how agents should coordinate. This paper introduces MAC (Multi-Agent Clarification), an interactive multi-agent framework specifically designed to resolve user ambiguities through strategic clarification dialogues, optimizing when, how, and by whom clarification should be initiated.
Executive Impact & Key Findings
MAC (Multi-Agent Clarification) revolutionizes how conversational AI systems handle user ambiguity, delivering significant performance boosts and operational efficiencies across diverse scenarios. For enterprise applications, this translates directly into enhanced customer satisfaction, reduced operational costs, and superior task completion rates.
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Task Success Rate Increase
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Dialogue Turns Reduction
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Performance vs. SOTA
Robust
Across All LLMs
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Architecture
Taxonomy
Performance
Benchmarks
Flexibility
Impact
MAC Workflow for Adaptive User Clarification
MAC introduces a novel multi-agent workflow where a Supervisor agent first handles high-level ambiguities, then routes to specialized Expert agents for domain-specific clarifications. This ensures a streamlined and efficient process for resolving complex user requests.
Enterprise Process Flow
User Query
→
Supervisor Agent: High-Level Clarification & Routing
→
Domain Expert Agent: Domain-Specific Clarification
→
API Execution & Response Generation
→
Task Completion
Role-Aware Clarification Taxonomy
A structured taxonomy guides agents on when and what to clarify. The Supervisor addresses general, domain-agnostic ambiguities, while Expert agents focus on detailed, domain-specific underspecifications, ensuring precise and contextually relevant interactions.
| Agent Role | Clarification Category | Examples |
|---|---|---|
| Supervisor | Domain/Intent Ambiguity, Vague Goal, General Conflicts |
|
| Expert | Parameter Underspecification, Value Ambiguity, Constraint Conflicts, Entity Disambiguation |
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Impact of Coordinated Clarification
Empowering both Supervisor and Expert agents to ask targeted clarification questions leads to a significant increase in task success while simultaneously reducing dialogue length, proving the efficiency of proactive, coordinated interaction.
+7.8%
Absolute Task Success Rate Gain
Enabling clarification at both levels increases task success rate from 54.5% to 62.3% and reduces average dialogue turns from 6.53 to 4.86, demonstrating superior efficiency and accuracy.
MAC's Superiority over SOTA TOD Systems
MAC sets a new benchmark for task-oriented dialogue systems by significantly outperforming previous state-of-the-art models on MultiWOZ 2.4, underscoring the power of its multi-agent architecture and effective clarification strategies.
| Method | Success Rate (%) |
|---|---|
| SimpleTOD | 22.00 |
| UBAR | 26.80 |
| AutoTOD | 46.90 |
| MAC (This Work) | 58.40 |
Robustness Across Diverse LLMs
MAC's modular design ensures robust performance across both proprietary (GPT-4o) and open-source (Qwen3-235B-A22B) LLMs. Notably, it delivers even larger accuracy gains for open-source models, narrowing the performance gap and making them viable alternatives for agentic systems.
Open-Source LLM Performance Boost
The framework's ability to leverage agent coordination and structured clarification significantly enhances the capabilities of less powerful models, making it a flexible solution for various enterprise deployments. This enables organizations to choose models based on cost and compliance without sacrificing performance. Qwen3-235B-A22B shows a +7.28 point gain in success rate with MAC clarification.
Critical Role of Supervisor for High-Level Ambiguity
The supervisor's ability to handle high-level, common-sense ambiguities (Domain Ambiguity, Intent Ambiguity, Vague Goal) is paramount. Ablation studies show that removing this capability causes the largest drop in task success, proving its indispensable role in robust multi-agent dialogue.
-6.20%
Success Rate Drop (without Supervisor Handling)
Ablating Ambiguity and Vagueness Handling from the supervisor yields a substantial drop in task success rate (-6.20%), confirming its critical role for robust multi-agent dialogue before delegation to domain-specific experts.
Calculate Your Potential ROI
Estimate the impact MAC could have on your enterprise operations by reducing ambiguity and improving task automation.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Strategic Implementation Roadmap
A phased approach to integrating MAC into your enterprise conversational AI, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Assessment
Conduct a thorough analysis of current conversational AI challenges, identify key ambiguity types, and define success metrics tailored to your business objectives.
Phase 2: Pilot Deployment & Customization
Implement MAC in a controlled pilot environment, customizing clarification taxonomies and agent roles to align with your specific domain knowledge and user interaction patterns.
Phase 3: Iterative Optimization & Scaling
Gather feedback, analyze performance data, and iteratively refine agent behaviors and clarification strategies. Gradually scale MAC across more conversational agents and domains.
Phase 4: Continuous Improvement & Monitoring
Establish ongoing monitoring and feedback loops to adapt MAC to evolving user needs and business requirements, ensuring sustained high performance and user satisfaction.
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