Workforce Sentiment That Closes The Loop
Ask how engagement is trending, which locations and segments are sliding, and what people are saying — by company, location or segment, never one person's sentiment. Every write is confirmed first.
Why Engagement Data Sits On A Dashboard Nobody Acts On
Mango Signal Agent attacks all of these — engagement health with signal-type breakdown, risk segments sorted worst-first, top themes ranked by signal count + polarity, and action-impact summaries that close the loop. Always aggregated; never individual sentiment.
One Score, No Signal Type Breakdown
"Engagement is 0.62." OK — driven by what? Pulse surveys? Recognition activity? News-feed sentiment? Manager 1:1 capture rate? Without the contributing signal types broken out, managers can't tell whether to fix communication, staffing, or something else entirely.
"Which Teams Are Sliding?" Is A Pivot Table Nobody Maintains
Some segments are below threshold this month. Some are below threshold every month. Some are sliding fast. Without a worst-first list ranked by trend, leaders end up reviewing the same five "always struggling" teams and missing the four that just started slipping.
Themes Get Captured, Then Lost
Pulse survey results, news-feed comments, recognition patterns — every signal has a theme, but the themes are scattered across three apps. The "shift-swap denials" theme is hot in fulfillment; nobody on the people team sees it until it shows up in attrition data.
Actions Get Taken, Outcomes Never Get Measured
The people team added a flex schedule, ran a recognition push, and reorganized two teams. Did the engagement score move? Nobody asks the question because the answer requires three saved views and a CSV pull. The action-feedback loop dies.
Survey Comments Sit Unread Because Reading Them Doesn't Scale
The pulse survey ran 1,800 free-text comments this month. Reading them all takes a day; most people skim 30, miss the thematic shift, and write a summary that confirms what they already believed. Aggregated theme rollups with polarity would surface the new pattern — but only if something is doing the aggregation between cycles.
Leadership Asks "How Are People Feeling?" And Gets Anecdotes Back
The CEO asks the people team for a read on morale before an all-hands. The honest answer requires engagement scores by team, recent theme shifts, recognition velocity, and exit trends — synthesized into three sentences. Without an aggregated read, the answer is whatever the people lead heard in their last six 1:1s.
Mango Signal Agent At A Glance
Mango Signal AI
Engagement health, risk segments, sentiment themes, action recommendations.
Inside Mango Signal Agent — The Actual Capabilities
Every block below maps to a real tool. Seven reads cover engagement health, theme ranking, risk segments, action listing + detail, recommendations, and impact summary. Two writes — create a Mango Signal action and update its status — both require explicit confirmation.
Engagement Health By Scope — With Signal Type Breakdown
The agent returns the rolling 0..1 engagement score for any scope — company, location, or segment — over a configurable window (default 30 days). Same response includes the contributing-signal-type breakdown (sentiment, recognition, performance, engagement, themes) so leaders see what's driving the number, not just the number.
- Engagement health via
get_engagement_health_score— rolling score for company / location / segment. - Top themes via
list_top_themes— ranked by signal count, with polarity (positive / neutral / negative) and avg score. - Always aggregated — scopes never resolve below a location or segment; individual sentiment is never returned to the LLM.
- Tenant-configurable threshold — what counts as "at risk" comes from tenant config; the agent uses the same threshold the app dashboard does.
Risk Segments — Worst-First, With Recommended Actions
The agent surfaces segments below the configured engagement threshold, sorted worst-first. For any segment, the agent generates 1-3 candidate next-step actions (title, description, due-in-days) anchored in the originating signals. Recommendations are suggestions; creating an action is a separate, confirmation-gated step.
- Risk segments via
list_risk_segments— below-threshold scopes sorted worst-first, with originating themes. - Action recommendations via
recommend_actions— 1-3 candidate actions per segment, anchored in originating signals. - Recommendations are not writes — the agent recommends; creating an action requires a separate confirmation-gated call.
- Privacy-by-design — recommendations are framed at the segment level; no individual employee names appear in prompts.
Create + Close-The-Loop Actions
Two risky writes turn recommendations into reality. create_mango_signal_action creates a tracked action assigned to the person asking (or someone they name) with optional links to originating signals; update_mango_signal_action_status moves the action through in_progress / complete / dismissed. Marking complete fires the close-the-loop update — company-wide for a company action, to the people at a location for a location action.
- Create action via
create_mango_signal_action— title, description, due date, originating signal IDs (RISKY · gated). - Update status via
update_mango_signal_action_status— in_progress / complete / dismissed; complete fires close-loop comms (RISKY · gated). - List + detail via
list_actions+get_action— filter by status / assignee; read-only. - Impact summary via
get_impact_summary— counts of measured / positive / negative impacts and avg score delta for the window.
Outcomes People Leaders Can Measure
The agent's job is to make engagement data operational — risk-surfacing to action, action to measured outcome, outcome to closed loop. Measure against your pre-agent baseline.
- Time-from-risk-to-action — days from a segment crossing the threshold to a Mango Signal action being created.
- Action close-rate — share of created actions reaching status=complete vs dismissed or stalled.
- Engagement score lift after intervention — average delta on the affected segment 30 / 60 / 90 days after action close.
- Theme conversion — share of top negative themes that translated into a tracked action within 14 days of surfacing.
- Close-the-loop comms reach — share of completed actions where the affected segment received the outcome comms.
Two Risky Writes · Aggregated By Design · Confirmation-Gated
Mango Signal Agent has just 2 risky tools — create and update an action. Both require explicit user confirmation. Every read is aggregated to company / location / segment; the agent never exposes individual employee sentiment to the LLM. The agent surfaces signal; managers own the action; the close-the-loop update tells the people the action is about.
- 2 risky write tools —
create_mango_signal_action,update_mango_signal_action_status— both confirmation-gated. - Aggregated by design — scopes never resolve below a location or segment; individual sentiment is never returned to the LLM.
- Close-the-loop comms — marking an action complete fires the "you said, we did" update (company-wide for a company action, to a location's people for a location action) with optional completion notes.
- Permission-aware — members reach the company score and their own actions; segment and location detail is manager-tier; raw per-person signals are admin-gated.
- Audit trail on every action — every tool call logs the requesting user, the tool, and the argument names.
WHAT TEAMS TRY INSTEAD
The four alternatives — and why none of them see signal across surveys, recognition, and behavior at once
Most CHROs and people leaders reach for one of these four. None of them stick because none of them aggregate signal across surveys, recognition, pulse, and behavioral telemetry under one privacy-preserving aggregation rule.
ChatGPT or Claude on a survey CSV
General-purpose AI summarizing free-text comments
- Aggregates across surveys, recognition, pulse, and behavioral data — not one CSV at a time
- Enforces a minimum group size so no individual sentiment ever reaches the LLM
- Closes the loop with a "you said, we did" update after an action completes
Culture Amp Lumi, Lattice AI, Glint AI
Vendor-trapped people-analytics AI
- Joins recognition, attendance, leave and frontline-execution signals — not just the survey instrument
- Cohort breakouts respect the same role scoping as the rest of the platform — no separate ACL system
- Action items are tracked in the same platform with owners and due dates, not a standalone "improvement plan" portal
A custom people-analytics build on the warehouse
The "people analytics" team's six-month project
- Already shipped — no warehouse pipeline, no privacy-aggregation logic, no separate BI tool to license
- Privacy-aggregation enforced at the tool layer, not by hoping the analyst remembers the k-anonymity rule
- Live signal — not a quarterly survey refresh with a six-week analysis lag
The manual fallback — annual engagement survey + roll-up deck
A quarterly slide deck and a town hall
- Surfaces signal continuously, not annually — leaders see the shift weeks before the next survey wave
- Cuts the cohort breakouts the deck never quite had — by location and segment
- Turns "we hear you" into action items that close the loop with a "you said, we did" update
PLATFORM ADVANTAGE
Mango Signal Agent inherits everything the platform already runs
A standalone people-analytics platform has to plumb each of these. The agent gets them for free because the platform already does.
Cross-source signal
Reads surveys, shift feedback, recognition, performance, attendance, leave and frontline-execution data — a survey-only platform sees one slice.
Minimum-group aggregation
Scopes never resolve below a location or segment. Individual sentiment is never returned to the LLM — enforced at the tool layer.
Close-the-loop comms
Marking an action complete sends the "you said, we did" update through the Communications app — company-wide for a company action, to a location's people for a location action — one platform, one loop.
Role-aware scoping
Members reach the company score and their own actions; segment and location detail is manager-tier; raw per-person signals are admin-gated, and the anonymity floor withholds any group smaller than the tenant's configured minimum.
AI request log
Every read and write is logged to AiApiLog with the requesting user and the tool (AI request detail is kept 30 days).
Automatic model tiering
The platform assigns each agent one model tier suited to its work — set for the agent, not picked per question.
INDUSTRY FIT
Industries where embedded workforce intelligence moves the most weight
Mango Signal Agent matters most where engagement and retention costs are big and the survey cycle is too slow.
Retail
Cuts engagement by store and district — and puts the action in the right DM's hands the week the signal shifts.
Healthcare
Tracks engagement by unit with minimum-group aggregation — never exposes individual nurse or hospitalist responses.
Manufacturing
Watches plant-floor sentiment alongside attendance and frontline-execution signal — a survey vendor can't see operational behavior.
Hospitality
Surfaces property-level engagement shifts before the seasonal turnover spike — not in the post-summer review.
Field Services
Joins technician sentiment with attendance, leave and missed-work signal — the operational levers that drive retention.
Public Sector
Agent activity is fully audit-logged. AI requests go to the platform's configured model providers.
WHY MANGOAPPS WINS
An embedded signal agent beats a chatbot, a survey-platform add-on, or a custom build on every axis
The argument CHRO, finance, IT, and people-analytics all share — and the one a horizontal AI or single-vendor add-on structurally cannot answer.
Cheaper than the alternatives
No Culture Amp or Lattice SKU, no per-seat ChatGPT, no six-month people-analytics warehouse — and the survey readout stops eating HRBP hours.
More secure
Minimum-group aggregation enforced at the tool layer, not the consultant deck. Individual sentiment never reaches an LLM — full stop.
Easier to deploy
Already deployed if Mango Signal is enabled. Turn the agent on, point it at the existing role scopes, and it's running the same day.
Easier to use
Lives in chat and inside the manager workflow — no separate engagement portal, no PDF download, no quarterly results meeting required.
Easier to manage
Per-business aggregation thresholds and scope rules sit in the same admin console as every other app's settings.
Easier to extend
Shares the agentic tool framework with every other MangoApps agent. New signal sources and new action types ship as tools, not rewrites.
AI is actually better
A horizontal or survey-platform AI can summarize comments. Mango Signal Agent also joins recognition, attendance, leave and frontline-execution signal — and closes the loop with comms back.
At our head office within HR, marketing, and finance, we use ChatGPT quite a bit, and MangoApps AI Helper is a useful tool for us. As I go around to the stores, I’ve been showing them how the AI Intranet Helper can find things and answer plain language questions about our resources, and they’re quite excited about that.
Alison van Zijl
Head of Human Resources
Full House
Customer Success
MangoApps Customers
Organizations that trust MangoApps to run every workflow across their workforce.
Customer Case Studies
Customer Case Studies
Customer Case Studies
Video Case Studies
Webinars
Customer Case Studies
Frequently Asked Questions About Mango Signal Agent
Nine tools — get_engagement_health_score, list_top_themes, list_risk_segments, recommend_actions, list_actions, get_action, get_impact_summary, plus 2 confirmation-gated writes: create_mango_signal_action and update_mango_signal_action_status.
No — by design. Every scope resolves to the company, a location, or a segment. The agent will refuse to return individual sentiment, and the underlying tools never pass individual signals to the LLM. This is the privacy posture every health / engagement / signal aggregation must have to be safe.
No. Both write tools (create_mango_signal_action and update_mango_signal_action_status) require explicit user confirmation before the agent runs them. Recommendations from recommend_actions are suggestions — creating the actual action is a separate, gated step.
Marking an action complete publishes a "you said, we did" update to the people it is about — everyone for a company-wide action, the people at a location for a location action — and emails the people whose feedback raised it. A segment action's update is posted only if whoever completes it asks for it to go to everyone. Optional completion notes are surfaced in the comms. This is how engagement data stops being a black hole and starts being a two-way conversation.
Time-from-risk-to-action, action close-rate, engagement score lift after intervention, theme conversion rate, and close-the-loop comms reach. Compare against your pre-agent baseline.
Let's Talk
Every team. Every employee. Every workflow. One AI platform — built for the frontline. Since 2008, trusted by 2 million+ users with an NPS of 78.
Why Choose Us?
- Frontline AI: Governed AI for every employee and workflow.
- Top Security: HITRUST, ISO & SOC 2 certified.
- Exceptional UX: Delightful on mobile and desktop.
- Proven Results: 98% customer retention rate.
Rolled out to every employee at AutoZone (125,000), PetSmart (50,000+), A.S. Watson and Raley's (20,000) — and at larger retailers we are not permitted to name.
Prefer to explore first? Ask AI about Mango Signal AI Agent →