The Future of AI: From Noise to Insight - An AI Agent for Customer Feedback

Microsoft

Oct 29, 2025

The Future of AI blog series is an evolving collection of posts from the AI Futures team in collaboration with subject matter experts across Microsoft. In this series, we explore tools and technologies that will drive the next generation of AI. Explore more at: Collections | Microsoft Learn

Every product team wants to be more customer-led. But the reality is messy: feedback pours in from all directions and in many formats—verbatims from surveys, “quick takes” in product, support tickets, community threads, and meeting notes. Important signals get buried in the noise. By the time a human can read, tag, and cross-reference everything, the moment for action has often passed.

So we asked a simple question: what if feedback analysis felt like a conversation instead of a chore? What if a teammate—available 24/7—could sift through the noise, surface what matters the most right now, and help product teams act immediately?

That’s the idea behind the customer feedback agent we created for our internal teams.

From scattered signals to clear, actionable insights

Our internal customer feedback agent:

Aggregates feedback from multiple approved channels into a consolidated, searchable view.
Understands content using modern language capabilities—clustering related themes, extracting key topics, summarizing long threads, and identifying sentiment and intensity.
Answers questions in plain language (e.g., “What are the top pain points for mobile onboarding this month?”), always linking back to the underlying evidence.
Prioritizes signals based on volume, recency, and impact heuristics—so teams see what’s trending and what’s urgent.
Recommends next steps, whether that’s a problem statement, an experiment, or a concise update for a status deck.
Helps close the loop with assisted response drafts that a human can review and send.

How we created our agent

Multi-agents: We’ve created our multi-agent workflow automation solution using Azure AI Foundry models, agents, and tools.
- Orchestrator agent: This agent interprets the user’s intent and routes the request to the appropriate specialist(s) based on feature, release, or channel, as well as data availability.
- Subagents (specialists): These include agents like the survey agent (for gathering insights from surveys), a prerelease agent [for identifying signals from our beta and user acceptance tests (UATs)], and a longform agent (for gathering notes and transcripts). Each sub-agent is aware of its own schema, retrieval strategy, and guardrails.

Our customization approach provides clear responsibilities for each agent, easier upgrades, and predictable routing. It also allows us to add or replace a sub-agent without disrupting the rest of the system.

Entry point: Microsoft Teams: The agent is available as a Teams app (bot)—accessible in chats, channels, and meetings—so users can ask questions and share results right where collaboration happens. Responses include links back to original evidence and offer one-click exports to a brief or slide.

Why this method? It brings insights directly into the collaboration hub our teams already use, so asking becomes second nature, not a separate task.

Data pipeline and indexing: We run scheduled ingestion jobs that normalize content, deduplicate entries, refresh a semantic + keyword index, and attach minimal metadata like product area, time window, and channel. The conversational layer retrieves context from this index before generating answers, improving speed, predictability, and compliance. Also, a simple, reliable refresh schedule outperforms a fragile web of real-time web of calls and makes governance clearer.

Human-in-the-loop actions: For outbound steps—like customer acknowledgments—the agent prepares drafts with the correct framing and headers. A human reviews, edits, and sends the message through the approved channels.

To keep humans in the loop—AI accelerates the process, but people make the decisions.

Architecture

For our multi-agent system, every interaction was designed to provide high quality outputs which can be used with confidence. Outbound communications and work item creation always includes a human review step to support quality and accountability. Every insight is fully traceable back to its source, so teams can validate with ease. And because privacy matters, all data is handled through governed pipelines with strict retention and compliance controls—keeping your organization secure while delivering actionable intelligence.

The problem it solves (before → after)

Area	Before	After
Triaging	Weeks of skimming, tagging, and copy pasting across systems.	Ask a question; get a concise, source-linked, synthesized answer in seconds.
Prioritization	Prioritization by anecdote; emerging issues are hidden under volumes of information.	Prioritization driven by structured signals (volume, recency, severity).
Follow-ups	Follow-ups are slow, so customers feel unheard.	Follow-ups are drafted with the right headers and tone—reviewed and sent by a human.

Next steps

This is just the beginning for our AI agent. We look to improve the agent by incorporating:

Richer actions: Automatically generate experiment templates or work item briefs that can be imported with a single click.
Longitudinal views: Visualize trend across releases, with overlays for severity and customer cohorts.
Additional channels: Expand to new feedback sources as governance and compliance permits.