agent-tuning

Optimize your ElevenLabs agent by selecting a low-latency voice model like eleven_flash_v2_5, crafting a system prompt with clear qualification objectives and constraints, tuning interruption sensitivity and response latency parameters, and running A/B tests to measure conversion rates and sentiment. This ensures natural conversations that maximize lead quality.

System prompt engineering for agents requires defining agent purpose, guidelines for acceptable responses, behavioral constraints, and context-specific rules. A well-crafted prompt directly guides agent behavior and conversation flow, reducing off-topic responses and improving alignment with business objectives like qualification or customer satisfaction.

Yes. A/B testing allows you to design experiments comparing different voices, prompts, or parameter configurations and track KPIs like qualification rate, sentiment, and user engagement. This data-driven approach identifies which configurations perform best for your specific use case.

Configure interruption sensitivity to control when agents yield to users, response latency to minimize delays between turns, and maximum duration limits to prevent overly long responses. These parameter adjustments create human-like turn-taking and reduce perceived wait times in conversations.

Voice selection depends on your target audience and use case context. Model choice balances latency and capability—eleven_flash_v2_5 prioritizes low latency for real-time interactions. Both affect how users perceive the agent's professionalism and responsiveness.

Yes. This approach applies specifically to real estate use cases, optimizing agents for lead qualification workflows, property inquiries, and prospect engagement. Configuration focuses on qualifying conversations, handling objections, and tracking conversion metrics relevant to real estate sales funnels.