Multi-Agent Automation for Aggregating NPS and CSAT Feedback Signals

This agent acts as the workflow's data hub, connecting to APIs from Qualtrics, Medallia, Zendesk, social listening tools, and call transcript systems. It ingests raw scores and unstructured comments, normalizes timestamps and customer IDs, and structures the data into a unified schema. It handles the high-volume, repetitive task of data collection and cleaning, which typically consumes 15-20 hours per week of analyst time.

Using fine-tuned NLP models, this agent performs multi-label classification on verbatim feedback, tagging each comment with specific drivers like billing_error, network_coverage, long_wait_time, or device_issue. It concurrently analyzes sentiment polarity and intensity. This replaces the manual coding and thematic analysis that delays insight generation by days, enabling real-time categorization of thousands of comments.

This agent correlates tagged feedback signals with operational data from network OSS (e.g., dropped call rates in a cell sector) and CRM (e.g., recent support tickets). It identifies statistically significant clusters—like a spike in slow_data complaints coinciding with a cell tower upgrade—and generates prioritized alerts. It automates the detective work that connects customer sentiment to operational root causes, a task usually done in weekly manual deep-dives.

Governed by a rules engine, this agent routes actionable insights to the correct downstream systems and teams. It creates Jira tickets for engineering teams, updates Salesforce cases for account managers, and pushes summarized dashboards to Tableau. It also generates compliance-ready weekly and monthly reports for leadership. This eliminates the manual swivel-chair process of copying data between spreadsheets, email, and presentation decks.

This agent manages the operational closure of the feedback loop. It monitors the status of tickets created from alerts and, when resolved, can trigger automated, personalized notifications back to affected customer segments (e.g., 'We've improved network speeds in your area'). This transforms feedback from a passive reporting exercise into an active customer retention tool, demonstrating responsiveness and building loyalty.

Built on a framework like LangGraph or Temporal, this is the central nervous system that sequences agent tasks, manages state, and handles failures. It provides full audit trails, performance metrics (e.g., agent latency, accuracy drift), and manual override points for human supervisors. This layer ensures the workflow is production-grade, reliable, and can be rolled out in phases—starting with a single channel like survey data before expanding to full omnichannel ingestion.

This team owns the business case and defines the actionable insights required. They specify which feedback signals (e.g., verbatim comments, low CSAT scores, social sentiment) should trigger specific operational responses, such as a process improvement ticket for engineering or a manager callback for a high-value customer. Their success metric is the reduction in manual analysis time and the speed of closing the loop from feedback to action.

Responsible for building the ingestion pipelines that unify fragmented feedback data from sources like Qualtrics, Medallia, Zendesk, Salesforce Service Cloud, and social listening platforms. This team architects the real-time streaming or batch data flows, ensures data quality and normalization, and exposes clean, timestamped signals to the agent orchestration layer. They manage the integration complexity with legacy CRM and operational systems.

This team designs and deploys the specialized agents using frameworks like LangGraph or CrewAI. They build the root-cause tagging agent that classifies feedback themes, the sentiment correlation agent that links survey scores to support interactions, and the routing agent that decides which team receives an insight. They implement the logic, guardrails, and observability (logging, tracing) for the entire multi-agent workflow.

The primary consumers of routed insights. This team receives automated alerts and synthesized reports on emerging complaint drivers, agent performance issues, or process gaps. They use the system to trigger targeted coaching, update knowledge bases, or adjust call-routing scripts. Their operational gain is moving from reactive complaint handling to proactive process improvement, reducing repeat contacts and improving first-contact resolution.

Receives automated, prioritized tickets when feedback analysis reveals technical root causes—such as a cluster of complaints about call drops in a specific area correlated with network KPI degradation. This closes the loop between customer sentiment and technical backlog, replacing slow, manual escalation processes with data-driven, automated issue detection and routing to the correct engineering squad.

Ensures the automated workflow adheres to data privacy regulations (e.g., GDPR, CCPA) when processing customer feedback. This team establishes approval gates for sensitive actions (like auto-issuing credits based on sentiment) and defines the audit trail requirements for all agent decisions. They work with the AI engineering team to implement necessary controls, ensuring the system is both effective and compliant.