Building a User Feedback Triage Agent for Mobile Growth Teams
Most mobile growth teams are drowning in feedback and acting on almost none of it. App Store reviews pile up, Zendesk queues grow, and every week someone manually skims a CSV export and writes a Slack summary that half the team ignores. The signal is there. The routing isn't.
A user feedback triage agent solves the routing problem. It ingests reviews, tickets, and in-app survey responses continuously, classifies each item by category and severity, enriches it with product context, and drops it into the right queue — engineering, growth, CX, or product — without a human touch. This post walks through the architecture, the prompt patterns that actually work, and the failure modes to design around before you ship.
What the Agent Needs to Do (And What It Doesn't)
Be precise about scope before you write a single prompt. A triage agent is not a resolution agent. It doesn't reply to users. It doesn't file Jira tickets automatically (unless you want it to, but start smaller). Its job is:
- Ingest feedback from multiple sources on a schedule or via webhook.
- Classify each item into a predefined taxonomy.
- Score severity so the highest-signal items surface first.
- Enrich with metadata — app version, platform, user segment if available.
- Route to the right destination (Slack channel, Linear board, HubSpot ticket, etc.).
- Summarize patterns weekly for the growth team's async standup.
That's six discrete tasks, and each one can fail independently. Knowing the boundary upfront keeps your prompts tight and your evaluation tractable. If you're not already thinking about evaluation, read our post on AI agent evaluation frameworks before you build — it'll save you a painful retrofit.
Architecture: Three-Layer Design
A reliable triage agent uses a three-layer architecture: ingestion, reasoning, and dispatch.
Layer 1 — Ingestion
Pull feedback from every source your team actually uses. Common sources for mobile growth teams:
- Apple App Store reviews (App Store Connect API or a scraper)
- Google Play reviews (Google Play Developer API)
- Support tickets (Zendesk, Intercom, Freshdesk webhooks)
- In-app NPS/CSAT responses (Typeform, Delighted, custom endpoints)
Normalize everything into a single schema before the LLM sees it. A flat JSON envelope works well:
{
"id": "applestore-9182736",
"source": "apple_app_store",
"platform": "ios",
"app_version": "4.2.1",
"rating": 2,
"text": "The checkout button disappears after I add a promo code. Happens every time.",
"user_segment": "returning_purchaser",
"received_at": "2026-09-27T08:14:00Z"
}
Do the normalization in code, not in the prompt. Every token you spend on format parsing is a token not spent on reasoning.
Layer 2 — Reasoning (The Agent Core)
This is where the LLM lives. Use a two-pass approach:
Pass 1 — Classification prompt. Give the model a fixed taxonomy and ask it to assign one primary category and one secondary category. A taxonomy for a typical consumer app:
| Category | Examples |
|---|---|
| Bug / Crash | force close, button broken, screen blank, login loop |
| Performance | slow load, lag, battery drain, high memory |
| UX / Friction | confusing flow, can't find feature, too many taps |
| Feature Request | wants dark mode, wants export, wants family sharing |
| Billing / Subscription | wrong charge, refund request, trial confusion |
| Positive Signal | love this app, best update yet, five stars with comment |
| Off-Topic / Spam | competitor mention, unrelated rant, gibberish |
Pass 2 — Severity scoring prompt. Feed the classification output back in and ask the model to score severity on a 1–5 scale using explicit criteria:
- 5 (P0): App is unusable for a core flow, data loss possible, affects multiple versions.
- 4 (P1): Significant friction on a high-traffic screen, multiple reports in 48 hours.
- 3 (P2): Noticeable issue, limited to one path, single report.
- 2 (P3): Minor annoyance, workaround exists.
- 1 (Info): Feature request or positive signal with no action required.
Keeping severity criteria in the system prompt (not the user message) means the scoring scale is consistent across every item the agent processes.
Deduplication. Before dispatching, run a lightweight semantic similarity check against recently routed items. Two-sentence embedding comparison with a cosine threshold of ~0.92 catches most duplicate bug reports without expensive LLM calls. This is a code-layer concern, not a prompt-layer concern.
Layer 3 — Dispatch
Route based on category × severity matrix. Here's a simple version:
| Category | Severity 4–5 | Severity 2–3 | Severity 1 |
|---|---|---|---|
| Bug / Crash | → #eng-incidents Slack + Linear P0 | → Linear backlog | → Discard |
| Performance | → #eng-incidents Slack | → Linear backlog | → Discard |
| UX / Friction | → #product-feedback Slack | → Notion database | → Notion database |
| Feature Request | → Notion database | → Notion database | → Notion database |
| Billing / Subscription | → Zendesk escalation queue | → Zendesk standard queue | → Zendesk standard queue |
| Positive Signal | → #growth Slack (weekly digest) | → Weekly digest | → Weekly digest |
The dispatch layer should be a simple router function in your orchestration code — not LLM logic. The model decides what something is; your code decides where it goes.
Prompt Patterns That Hold Up
Be explicit about output format. Ask for JSON, define the schema in the system prompt, and validate the output in code before it touches downstream systems. An unconstrained LLM will occasionally return markdown, prose, or a hybrid. That breaks your pipeline at 2 AM.
Give the model the taxonomy, not just a description of the taxonomy. Don't say "classify into relevant categories." Say "classify into exactly one of the following: Bug/Crash, Performance, UX/Friction, Feature Request, Billing/Subscription, Positive Signal, Off-Topic/Spam." Closed-ended classification prompts dramatically outperform open-ended ones on consistency.
Include a "reasoning" field in the output schema. Ask the model to explain its classification in one sentence before returning the label. This chain-of-thought step improves accuracy on ambiguous items and gives you a human-readable audit trail. It costs tokens but it's worth it.
Handle multilingual input explicitly. If your app has international users — and most apps do — your reviews will come in German, Portuguese, Korean, and a dozen other languages. Either instruct the model to translate before classifying, or use a multilingual-capable model and tell it to classify regardless of input language. Don't assume English-only input.
If your app's growth depends on understanding what users actually want, you need the right infrastructure to capture and act on that signal. Our mobile app marketing team builds the analytics and feedback loops that feed agents like this one.
Token Budget and Cost Reality
Running classification + severity scoring on every incoming feedback item adds up. In our engagements, a typical consumer app in the mid-growth stage receives roughly 200–800 pieces of feedback per week across all sources. At that volume, a two-pass approach using a mid-tier model (GPT-4o mini, Claude Haiku, or equivalent) costs approximately $2–$8/month — essentially nothing.
Where costs spike:
- Long support tickets. A 600-word support ticket costs roughly 10× more to process than a 60-word review. Truncate at a reasonable threshold (typically 400 tokens of user input) after extracting the key complaint.
- Batch summarization. Weekly pattern summaries that feed hundreds of items into one prompt can get expensive fast. Summarize in chunks, then summarize the summaries.
- Embedding-based deduplication at scale. If you're running embedding calls on every item, consider caching embeddings for 7 days and only re-embedding when the text changes.
For a deeper breakdown of agent cost modeling, see our post on what running an agent actually costs per month.
Failure Modes to Design Around
Classification drift. The model's interpretation of category boundaries will shift as you update prompts or switch model versions. Run a fixed eval set of 50–100 labeled items every time you touch the system prompt. If accuracy drops more than 5 percentage points on any category, investigate before deploying.
Severity inflation. Models trained on human feedback tend to be sympathetic — they'll overweight emotional language and assign P1 to items that are really P3. Calibrate by having the model compare each item against an anchor example per severity level. "This is a P4 bug. Evaluate the new item relative to it."
Missing metadata. App version and user segment are critical for deciding whether a bug is in a deprecated version or a fresh release. When that metadata is absent, the agent should flag the item as "metadata-incomplete" rather than guessing. Make absence explicit.
Routing to a dead end. A Slack channel that nobody monitors is worse than no routing at all — it creates false confidence that the signal is being handled. Audit your destination queues quarterly. If a channel hasn't generated a response to a routed item in 30 days, the routing rule is broken.
FAQ
How long does it take to build a feedback triage agent from scratch?
For a team with existing API access to their feedback sources and a clear taxonomy, a working prototype typically takes one to two weeks. A production-hardened version with monitoring, eval sets, and deduplication adds another two to three weeks.
Can this agent work across iOS and Android simultaneously?
Yes, and it should. Normalize App Store and Play Store review formats into the same schema at the ingestion layer. The classification and severity prompts don't need to know which platform the feedback came from — that's metadata, not signal for the reasoning model.
What model should I use for classification?
For classification and severity scoring, a mid-tier model (GPT-4o mini, Claude Haiku, Gemini Flash) is usually sufficient and significantly cheaper than frontier models. Reserve a frontier model for weekly pattern summarization where nuance matters more.
Do I need a vector database?
Not for most mobile apps at mid-growth scale. A simple embedding cache in Redis or even a flat file is enough to power deduplication at volumes under 5,000 items per week. A vector database adds operational complexity that isn't justified until you're doing semantic search across your full historical feedback corpus.
How do I handle feedback in languages other than English?
Instruct the model to detect the input language, translate the core complaint to English internally, and then classify. Include the original text in your stored record. Don't discard non-English feedback — it often surfaces issues in specific regional builds that the English-speaking team never sees.
What's the biggest mistake teams make when building this?
Skipping the eval set. Teams build the agent, do a quick manual spot-check, call it good, and ship. Three weeks later, severity inflation has routed 40% of P3 items to #eng-incidents and the engineering team has muted the channel. Build your labeled eval set before you write a single production prompt.
If you're building a mobile app and want custom AI agents that actually move the needle on growth — not demos that live in a notebook — our app development team builds them end-to-end. Or book 30 minutes with Marco to talk through your specific feedback volume, stack, and what's worth automating first.
