Overview

Teams collect feedback constantly, but acting on it is harder than it should be.

Comments, notes, survey responses, and messages often arrive fragmented, ambiguous, or emotionally charged. Before any action can happen, someone has to slow down, interpret what’s being said, and translate it into something usable.

This agent focuses on that missing step.

It treats feedback as raw input, applies a structured workflow, and produces clear, neutral outputs that support human decision-making without replacing it.

Why this exists

In many teams, feedback fails not because people don’t listen, but because:

• Input is vague or inconsistent

• Signals get mixed with opinions

• Urgency is debated instead of clarified

• Actionability is assumed rather than assessed

AI systems often make this worse by jumping straight to solutions.

This agent takes a different approach.

It pauses.

It structures.

It makes uncertainty visible.

What the agent does

This is a workflow-based system, not a conversational assistant.

Given one or multiple pieces of raw feedback, the agent:

• Classifies the type of feedback (UX issue, request, opinion, etc.)

• Assesses whether the input is actionable or ambiguous

• Extracts concrete issues using neutral product language

• Surfaces possible next investigative actions

• Indicates relative urgency based on language and context

The output is a structured table, suitable for export to Excel or Google Sheets.

An optional summary can also be generated for internal notes or email follow-up.

What the agent does not do

This system is intentionally constrained.

It does not:

• Redesign flows

• Propose solutions

• Argue priorities

• Replace product judgment

• Act autonomously

Its role is to support clarity, not authority.

Interaction model

• Input is treated as data, not as a prompt

• The workflow is deterministic and repeatable

• Outputs are structured artifacts, not chat responses

The agent can be used without login or setup.

Outputs

Primary output

• A structured table containing:
  

• Feedback type

• Area or journey stage

• Extracted issue

• Actionability signal

• Suggested next action

• Priority indicator

• Notes and assumptions

Export formats:

• Excel

• CSV

Optional secondary output

• A short, neutral summary suitable for internal sharing or email
  

What this project demonstrates

• Workflow design beyond chat interfaces

• Classification and normalization of messy language

• Ambiguity handling inside execution

• Guarded AI behavior

• Human-in-the-loop decision support

• Practical system boundaries

This project is designed to show how AI can support product thinking without overstepping it.

Design principles

• Treat language as signal, not instruction

• Make uncertainty explicit

• Separate interpretation from action

• Prefer structure over fluency

• Keep humans accountable for decisions
  

This agent is not designed to move fast.

It’s designed to help teams move deliberately.