Workforce Sentiment That Closes The Loop
"What's our engagement health this month?" "Which teams are sliding?" "What are people talking about?" "Did last quarter's actions move the needle?" — Mango Signal Agent answers all of it, scoped to team / segment / location (never individual sentiment). Two risky writes — create an action, update an action — both confirmation-gated. Marking an action complete fires the close-the-loop comms back to the affected segment.
Why Engagement Data Sits On A Dashboard Nobody Acts On
Mango Signal Agent attacks all four — 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 by scope, theme ranking, risk segments, action listing + detail, action recommendations, and impact summary. Two writes — create a Mango Signal action and update its status — both require explicit confirmation. Marking complete fires the close-the-loop comms to the affected segment.
Engagement Health By Scope — With Signal Type Breakdown
The agent returns the rolling 0..1 engagement score for any scope — company, segment, manager team, or location — over a configurable window (default 30 days). Same response includes the contributing-signal-type breakdown (pulse surveys, recognition, news-feed sentiment, manager 1:1 capture) so leaders see what's driving the number, not just the number.
- Engagement health via get_engagement_health_score — rolling score for company / segment / team / location / manager.
- Top themes via list_top_themes — ranked by signal count, with polarity (positive / neutral / negative) and avg score.
- Always aggregated — scopes never resolve below team / segment / location; 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 a manager (or self) 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 comms back to the affected segment.
- 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 team / segment / location; the agent never exposes individual employee sentiment to the LLM. The agent surfaces signal; managers own the action; the close-the-loop comms keeps the affected segment in the loop.
- 2 risky write tools — create_mango_signal_action, update_mango_signal_action_status — both confirmation-gated.
- Aggregated by design — scopes never resolve below team / segment / location; individual sentiment is never returned to the LLM.
- Close-the-loop comms — marking an action complete fires comms back to the affected segment with optional completion notes.
- Permission-aware — managers see only their team's signals; segment / location aggregates respect role-based scope.
- Audit trail on every action — every tool call logs the requesting user, the tool, and the parameters, attached to the action record.
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 team-minimum aggregation so no individual sentiment ever reaches the LLM
- Closes the loop with comms back to the affected segment after an action completes
Culture Amp Lumi, Lattice AI, Glint AI
Vendor-trapped people-analytics AI
- Joins recognition, training completion, and operational telemetry — not just the survey instrument
- Cohort breakouts respect the same role scoping as the rest of the platform — no separate ACL system
- Action items thread back to the operational app that resolves them, 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 team, segment, location, manager
- Turns "we hear you" into action items that close the loop with comms back to the segment
PLATFORM LEVERAGE
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, recognition, pulse, training, and operational behavioral data — a survey-only platform sees one slice.
Team-minimum aggregation
Scopes never resolve below team / segment / location. Individual sentiment is never returned to the LLM — enforced at the tool layer.
Close-the-loop comms
Marking an action complete fires comms back to the affected segment through the News Feed and Comms Hub apps — one platform, one loop.
Role-aware scoping
Managers see only their team's signals; segment / location aggregates respect the same role-based scope as the rest of the platform.
Audit trail & retention
Every read and write lands in AiApiLog with the same retention and eDiscovery posture as the rest of the platform.
RubyLLM-grounded model tiering
Nano / small / medium / standard tier selection routes routine cohort pulls to cheap models and reserves the big ones for cross-source reasoning — automatically, per call.
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 routes manager-action items to the right DM the week the signal shifts.
Healthcare
Tracks burnout signal by unit and shift type with team-minimum aggregation — never exposes individual nurse or hospitalist responses.
Manufacturing
Watches plant-floor sentiment alongside safety-incident and training-completion 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 on-call rotation load and route data — the operational levers that drive retention.
Public Sector
Runs entirely inside FedRAMP-eligible deployment options with full audit logging — no employee signal leaving the tenant boundary.
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, no extra HRBP to triage the survey readout.
More secure
Team-minimum 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, scope rules, and audit retention 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. Only Mango Signal Agent can also join recognition, training, and operational telemetry — and close 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
Related Customer Stories
Video Case Studies
Customer Case Studies
Customer Case Studies
Video Case Studies
Customer Case Studies
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 team, segment, or location. 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 fires comms back to the affected segment — letting the team that surfaced the signal know what happened in response. 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
Since 2008, we've been building the workforce platform — earning the trust of 2 million+ users and an NPS of 78.
Why Choose Us?
- AI-Powered Platform: The most unified workforce experience on the planet.
- Top Security: HITRUST, ISO & SOC 2 certified.
- Exceptional UX: Delightful on mobile and desktop.
- Proven Results: 98% customer retention rate.
Trusted by Legendary Companies:
