Production-GradeAIAgentsforCustomerEngagementandOperationalWorkflowAutomation.

Production voice agents are engineered for sub-1.2-second end-to-end latency under standard network conditions. Latency encompasses speech-to-text processing, language model inference, tool execution, and text-to-speech synthesis. Conversational agents on text channels typically respond within 800 milliseconds. Latency benchmarks are validated during conversation testing prior to production deployment.

Runtime costs are usage-based, calculated per minute for voice agents or per conversation for text-based agents. Costs include pass-through of AI vendor charges — speech processing, language model inference, text-to-speech synthesis, and vector retrieval — plus Dharwin operational margin for monitoring, alerting, and optimization. Projected runtime economics are modeled during discovery based on anticipated conversation volume.

Voice and conversational agents support multilingual deployment across major business languages. Language capability depends on selected speech-to-text, language model, and text-to-speech providers. Multilingual requirements are assessed during discovery, with language-specific testing conducted prior to production deployment.

Agents operate with defined confidence thresholds. When confidence falls below configured levels, when policy requires human review, or when the user explicitly requests escalation, the agent initiates a structured handoff. The human operator receives full conversation transcript, customer record context, and a recommended next action. All escalation events are logged for review and agent optimization.

Agents can be deployed in Dharwin-managed infrastructure or within client-controlled environments, subject to integration and security requirements. Deployment options — including data residency, network boundaries, and access controls — are defined during discovery and documented in the agent specification.

A production AI agent conducts structured multi-turn conversations, executes operational actions within connected systems, maintains context across interactions, and includes documented escalation behavior. Standard chatbots typically provide scripted responses without system integration, tool execution, or governed handoff. Dharwin agents are engineered for operational outcomes — qualification, scheduling, transaction execution — rather than informational deflection alone.

Production agents include real-time monitoring dashboards, alerting for error rates and latency thresholds, and scheduled performance reviews. Conversation analytics cover completion rates, escalation frequency, tool execution success, and knowledge base retrieval accuracy. Optimization recommendations are provided at defined review intervals.

A single production agent — from discovery through deployment — typically requires six to eight weeks. Timeline varies based on conversation complexity, integration count, knowledge base scale, and testing requirements. Multi-agent engagements are sequenced with parallel workstreams where integration dependencies permit.