Agentic System for Real-Time Agent Performance Support and Script Guidance

This workflow automates the most costly bottleneck in utility contact centers: the manual search for scripts, rate codes, and outage protocols during live calls. By deploying a real-time speech-to-text pipeline connected to a RAG system over the CIS and knowledge base, an AI co-pilot surfaces precise guidance to the agent's desktop. The operational upside is direct—reducing average handle time by 15-25% and improving first-call resolution for complex billing or outage inquiries by ensuring policy accuracy and reducing transfer rates.

Implementation requires integrating a low-latency speech API (e.g., Azure Speech, Google Speech-to-Text) with an orchestration layer (LangGraph) that routes transcribed intents to a vectorized knowledge base and live CIS/OMS queries. The desktop interface must be built into the existing agent CRM (e.g., Salesforce Service Cloud, Nice CXone). Critical controls include configurable confidence thresholds for suggestions, a silent monitoring mode for supervisor review, and an override log to track when agents deviate from AI guidance for continuous model tuning.

This workflow automates the cognitive load of searching knowledge bases and recalling complex procedures during high-pressure customer interactions. By listening to the conversation via speech-to-text, an orchestration agent retrieves the most relevant policy documents, troubleshooting scripts, and next-best-action recommendations from the CIS and knowledge management systems. It surfaces this guidance through a desktop interface, enabling the human agent to resolve issues faster without switching contexts, directly reducing average handle time (AHT) and improving quality scores.

Implementation requires integrating a low-latency speech-to-text pipeline, a retrieval-augmented generation (RAG) system over internal documentation, and a secure desktop overlay. The orchestration agent, built with frameworks like LangGraph, must manage state, handle exceptions, and log all guidance for QA. Controls include configurable confidence thresholds for automated suggestions, supervisor override capabilities, and continuous monitoring of suggestion acceptance rates to refine the model and ensure it augments rather than disrupts the agent's workflow.

Phase 1 establishes the core orchestration layer, integrating with the Customer Information System (CIS) and contact center telephony via secure APIs. This foundational build focuses on real-time speech-to-text transcription and context extraction, enabling the AI to listen to calls and identify key intents like outage reporting or billing disputes. A lightweight desktop interface is deployed to a pilot group of agents, delivering script suggestions and policy lookups with minimal latency, providing immediate validation of the workflow's impact on handle time.

Phase 2 expands the system's intelligence, adding a multi-agent architecture for specialized tasks: one agent retrieves restoration estimates from the Outage Management System (OMS), another validates meter status, and a third manages compliance-sensitive escalations. Rigorous controls are implemented, including supervisor alerting for low-confidence suggestions and an audit trail logging all AI-generated guidance. The final phase integrates with quality assurance and workforce management platforms, using call outcomes to retrain the suggestion models and forecast support demand, creating a closed-loop system that continuously improves agent performance and operational efficiency.

This workflow directly targets the operational bottleneck of complex outage and billing calls, where agents must navigate multiple systems while maintaining compliance. The AI agent listens to the live conversation, retrieves relevant policy documents and scripts from the CIS and knowledge base, and surfaces precise next-step suggestions via a desktop overlay. The business value is clear: a 15-25% reduction in average handle time and improved accuracy on first contact, directly lowering call-center labor costs and increasing customer satisfaction (CSAT).

Implementation requires integrating a real-time speech-to-text pipeline, a low-latency retrieval system for utility documents, and a secure desktop interface. Key controls include configurable confidence thresholds for suggestions, a mute/override function for the human agent, and full audit logging of all AI-suggested actions. A phased rollout would start with non-critical billing inquiries, gather performance data, and gradually expand to outage triage, ensuring the system enhances rather than disrupts high-stakes operations.