Ask What Needs You This Shift
A worker asks what needs them right now and gets their ranked day. A district lead asks which stores are lagging and gets a coverage rollup bounded to their own slice of the location tree.
HOW IT WORKS
How it answers a shift question
From a plain question to a grounded answer — using the same campaigns, location scope, and completion rules the app already enforces. It never sees data the asker couldn't already see.
1. Scope
Every read is bounded by the asker's slice of the location tree and their business. A store lead's "which sites are lagging" is their sites, resolved server-side.
2. Read
Eight read-only tools cover the ranked day, campaigns, coverage rollups, shift briefs, assignment history, pending reviews, and open requests.
3. Confirm
Every state-changing action — draft, launch, remind, close, accept, reject, complete — shows the parsed action and waits for an explicit confirmation.
4. Log
Anything the agent does lands in the campaign's decision history flagged as an AI actor, while still naming the person it acted for.
An AI action is never an anonymous action
Decision history distinguishes three kinds of actor — person, system, and AI. An action the agent took is recorded as AI, and it still names the human it acted for. Automatic decisions the platform made on its own are recorded as system rather than attributed to whoever happened to be logged in at the time.
- Three actor kinds — person, system, and AI, never collapsed into one.
- AI actions name their principal — flagged as AI, attributed to the person who asked.
- System actions aren't blamed on a human — an automatic handoff isn't recorded as someone's decision.
- Append-only and exportable — the history can't be edited, and it outlives the campaign.
The agent is bound by the same gates as the screen
Reads are scoped to the asker's locations. Authoring tools refuse for anyone who couldn't author in the UI. Review tools enforce separation of duties — the agent will not let someone accept work they did themselves, exactly as the review queue won't. And a completion asked for through chat goes through the same completion policy as a tap on the phone, including the case where the channel has no location at all.
- Location-scoped reads — the agent cannot widen someone's view of the tree.
- Authoring gated — draft, launch, and close refuse for non-authors.
- Separation of duties enforced — no accepting your own work through chat.
- Locationless channel handled honestly — a completion that needs a location says so and points at the mobile app, instead of quietly passing.
Where HQ-To-Field Execution Loses Time
Frontline Execution AI attacks the specific lookups that eat a store lead's morning and a district lead's week — without changing the targeting, proof rules, or review routing the app already enforces.
"What Do I Actually Need To Do Right Now?"
A worker opens the app mid-shift with fifteen minutes free. Some of it is a campaign, some is a task, some is a must-read, one is training due Friday. Deciding what to do first is a job in itself — and the wrong answer is the one that was due an hour ago.
District Coverage Lives In A Spreadsheet Pull
A district lead wants to know which of their twenty stores are behind on the changeover. The number exists, but getting it means opening the app, filtering to their district, sorting, and reading. So it gets checked once a week instead of once a morning.
Launches Land On Already-Loaded Teams
HQ schedules a directive for Thursday without knowing that the same stores already have two campaigns, a training deadline, and an unread must-read landing that day. Overload shows up as a completion-rate dip a week later, with no obvious cause.
"Why Is This Assigned To Me?" Has No Fast Answer
A worker inherits a piece of work mid-shift and doesn't know why. Tracing it means asking a manager, who asks someone else. The ledger has the answer — who held it and why it moved — but nobody queries a ledger from the floor.
Review Queues Are Checked When Someone Remembers
Submitted work sits waiting because the reviewer doesn't know it's there. The queue is a page you have to decide to open, not a thing that tells you it has three items in it.
Requests Pile Up Between Ops Standups
A department asks for a campaign. It waits in the request funnel until the ops team next opens it. The lead time the request needed quietly expires while it's sitting there.
Frontline Execution AI At A Glance
Frontline Execution AI
Ranked days, coverage rollups, workload previews, and gated campaign writes.
Inside Frontline Execution AI — The Actual Capabilities
Every block below maps to a real tool the agent runs against your Frontline Execution records. 8 tools are read-only; 7 writes — drafting, launching, reminding, closing, accepting, rejecting, and completing your own item — are confirmation-gated.
The Worker's Question — What Needs Me This Shift
The frontline lookup, answered in one turn. The agent returns the same ranked day the app shows — overdue first, then critical, then the rest — with the reason each row is ranked where it is. A shift brief widens it to what matters at a whole location right now.
- get_my_day — the asker's ranked obligations for the current shift, with the ranking reason on each row.
- shift_brief — what matters at a location right now, for the person about to walk the floor.
- get_assignment_history — who held a piece of work and why it moved, straight from the append-only ledger.
- Critical cap respected — the agent honors the same per-person daily cap on critical items as the app.
The District Question — Which Stores Are Lagging
Coverage without a spreadsheet pull. The agent returns completion rolled up the location hierarchy, bounded to the asker's own slice of the tree — so a district lead gets their district, and a regional lead gets their region, with no filter to remember to set.
- get_campaign_coverage — completion by location for a campaign, worst first.
- list_campaigns — active, scheduled, and recently closed directives in scope.
- Location-scoped server-side — the agent cannot return a location the asker couldn't already open.
- Same numbers as the page — coverage counts real minted items, not a chat-time recomputation.
The Ops Question — What Would This Launch Cost The Floor
Before recommending a launch, the agent checks what work is already landing on that audience — across campaigns, ordinary tasks, unread must-reads, and due training — and will tell you to stagger it if the group is loaded. Overload becomes an authoring-time fact rather than a post-mortem finding.
- preview_audience_load — what the target audience already carries, before you add to it.
- Names unknowns — work with no time estimate is counted separately rather than silently treated as zero.
- Names degraded sources — if a source app can't be reached, the preview says so instead of under-reporting.
- Advisory, not blocking — the gauge informs the decision; the human still makes it.
Campaign Writes — Draft, Launch, Remind, Close
The agent can carry a directive from a plain-language description through to launch, nudge the people who haven't started, and close it out — but never without an explicit confirmation, and never for someone who couldn't do it in the UI.
- draft_campaign — gated write. Turn a plain-language directive into a campaign draft; never auto-submits.
- launch_campaign · close_campaign — gated writes. Publish or close, subject to the same authoring permission as the UI.
- remind_pending — gated write. Nudge everyone who hasn't started, without opening the roster page.
- list_campaign_requests — read. What the department request funnel is waiting on.
Review Writes — Accept, Reject, And Your Own Completion
The reviewer's queue, answerable in chat. The agent surfaces what's waiting, accepts work, or sends it back with a reason — and refuses to let anyone approve work they did themselves, exactly as the review queue does. A worker can also complete their own item, through the same completion policy as a tap on the phone.
- list_pending_reviews — read. What's submitted and waiting on the asker.
- accept_work · reject_work — gated writes. Separation of duties is enforced; a rejection requires a reason the worker can act on.
- complete_my_item — gated write. Runs the same completion policy as every other channel — permission, role, shift, site, training.
- No location in chat — a completion needing site verification says so and points at the mobile app, rather than quietly passing or quietly failing.
Outcomes Teams Can Measure
The agent is built to compress the "what's on me right now" lookup, put coverage in front of district leads daily instead of weekly, catch audience overload before a launch, and shorten the review loop. Measure against your pre-agent baseline.
- On-time completion rate — share of campaign items completed inside their window, trending against baseline.
- Time-to-first-action on a shift — minutes from opening the app to completing the first item.
- Review latency — hours submitted work waits before accept_work or reject_work.
- Launches preceded by a workload check — share of launches where preview_audience_load ran first.
- Unassigned-item dwell time — how long an obligation sits without an owner before it's resolved.
7 Gated Writes, Permission Path Always Enforced
Frontline Execution AI has 15 tools. 8 are read-only — the ranked day, campaigns, coverage, workload preview, shift brief, assignment history, pending reviews, and open requests. The 7 writes — draft, launch, remind, close, accept, reject, and complete-my-item — each require explicit confirmation, and each refuses for anyone who couldn't take that action in the UI.
- 7 gated writes — every state-changing action shows the parsed action and waits for confirmation.
- Authoring and review permissions unchanged — the agent is a client of the same gates, not a bypass.
- Separation of duties holds in chat — including for administrators, and unconditionally on proof-bearing campaigns.
- Every action audit-trailed — recorded in the campaign's decision history as an AI actor, naming the person it acted for.
WHAT TEAMS TRY INSTEAD
The alternatives — and why none of them know who's on shift, who's certified, or who's on leave
When operations wants a faster answer than opening the app, they reach for one of these. None of them are bounded by the asker's location scope, enforce separation of duties, or write back into an auditable decision history.
Pasting a store list into ChatGPT, Claude, or Copilot
General-purpose AI reasoning over a copied spreadsheet
- Runs against the live campaign records — not a stale paste — with location scope, roster, leave, and certifications intact
- Seven gated writes actually execute the workflow (draft, launch, remind, close, accept, reject) rather than producing advice
- Nothing leaves the tenant, and every action lands in the campaign's decision history
A store-execution vendor's built-in assistant
Vendor-trapped AI inside a single execution silo
- Joins execution to Shifts (who's on), Training (who's certified), Leave (who's off), Inspections, and Service Desk — vendor AI sees only its own task table
- Frontline workers, store leads, and HQ ops share one chat surface, with reads bounded per person by the same location scope
- Survives an execution-vendor migration; the agent's tools repoint at the platform, not at a contract
A custom internal ops bot
An ops team's build, then forever maintenance
- Shipped already — nobody has to rebuild location scoping, the completion policy, or separation of duties in a bot
- Confirmation gating, audit trail, and permission parity with the UI come with the platform rather than being bolted on later
- Inherits new capabilities as the app evolves, instead of drifting from it the first time the app ships a feature
The manual fallback — "open the app and filter"
The default when the AI options fall short
- The ranked day and the coverage rollup arrive in one turn, so they get checked every morning instead of every week
- Workload previews run before a launch rather than after a completion-rate dip
- Pending reviews and open requests surface on their own instead of waiting for someone to remember the page exists
PLATFORM ADVANTAGE
Frontline Execution AI inherits everything the platform already runs
A custom-built execution agent has to plumb each of these. Frontline Execution AI gets them for free.
Cross-app data plane
Joins campaigns with Shifts (who's on), Training (who's certified), Leave (who's off), Tasks, Inspections, and Service Desk — one chat surface for the whole execution picture.
Location-scoped reads
Every read is bounded server-side by the asker's slice of the location tree — the same scope the coverage page uses. The agent cannot widen it.
Confirmation-gated writes
All seven writes show the parsed action and wait for explicit confirmation, and refuse outright for anyone who couldn't take that action in the UI.
Audit trail & retention
Every action lands in the campaign's append-only decision history flagged as an AI actor, with the same retention posture as the rest of the platform.
Translation in 100+ languages
Workers ask in their working language; campaign content already translates into each worker's profile language, labelled as machine translated.
RubyLLM-grounded model tiering
Routine day and coverage reads run on cheaper tiers; campaign drafting routes higher. Automatic per call, no per-tenant tuning.
INDUSTRY FIT
Industries where execution AI moves the most weight
Frontline Execution AI earns its keep wherever the same directive lands in hundreds of places and someone has to know, today, whether it happened.
Retail & Convenience
Store leads ask what's on them this shift; district leads ask which of their sites are behind on the changeover — both answered from live coverage.
Grocery & Pharmacy
Directives targeted by store attribute, with coverage rollups that answer "which pharmacy sites finished" without a filter to remember.
Restaurants & QSR
Opening and closing checklists surface as a ranked day, and a manager can nudge everyone who hasn't started without opening the roster page.
Manufacturing
Certification-gated procedures answer honestly in chat — the agent will not complete work whose training prerequisite can't be confirmed.
Healthcare & Facilities
Per-shift rounds and their handoffs are traceable in one question — who held this, and why did it move to me.
Logistics & Distribution
Regional leads get coverage bounded to their region, and ops previews audience load before adding another directive to a loaded week.
WHY MANGOAPPS WINS
An embedded execution agent beats a vendor assistant, a horizontal chatbot, or a DIY bot on every axis
The argument operations, IT, and the frontline all share — and the one a single-vendor execution AI structurally cannot answer.
Cheaper than the alternatives
No per-seat horizontal AI license, no separate execution-vendor assistant contract, no six-month DIY bot, no ops headcount soaked by coverage pulls.
More secure
Location-scoped reads, seven gated writes, separation of duties enforced in chat, and an audit trail on every action. Nothing leaves the tenant.
Easier to deploy
Already deployed if Frontline Execution is enabled. Turn the agent on and workers get their ranked day in chat the same day.
Easier to use
One chat surface for the day, campaigns, coverage, workload, reviews, and requests — in the app frontline staff already carry for shifts and pay.
Easier to manage
Permissions, location scope, and the completion policy live in the app's own settings. The agent is a client of them, so there's no second access model to maintain.
Easier to extend
New tools ship as agent capabilities as the app grows — no DIY rewrite each time targeting, proof, or review routing changes.
AI is actually better
A vendor assistant can list open tasks. Only this agent also knows who's rostered, who's on leave, who holds the certification, what the audience already carries — and refuses the writes that would break separation of duties.
Our frontline employees determine whether or not a customer is going to come back to the store. In order to provide a great customer experience, you’ve got to provide a great experience to your team members. If you’re not engaging with them, it’s less likely that they’re going to deliver on this, simply because they haven’t been enabled and supported to do it.
Chelsea Carbahal
VP, Community Impact and Public Affairs
Raley's Supermarkets
Customer Success
MangoApps Customers
Organizations that trust MangoApps to run every workflow across their workforce.
Customer Case Studies
Customer Case Studies
Video Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Frequently Asked Questions About Frontline Execution AI
15 tools across HQ-to-field execution — your ranked day for the current shift, campaign lists, a campaign's coverage rollup by location, an audience workload preview, a location shift brief, an item's assignment history, pending reviews, and open campaign requests (all read-only); plus drafting, launching, reminding, and closing a campaign, accepting or rejecting submitted work, and completing your own item (all confirmation-gated).
No. All seven writes — draft_campaign, launch_campaign, remind_pending, close_campaign, accept_work, reject_work, and complete_my_item — show the parsed action and require explicit confirmation before running. They also refuse outright for anyone who couldn't take that action in the UI.
No. Separation of duties is enforced in chat exactly as it is in the review queue — including for administrators, and unconditionally on any campaign that required photo or signature proof. The agent is a client of the same gate, not a way around it.
No. Every read is bounded server-side by the asker's slice of the location tree, using the same scope the coverage page uses. A district lead's "which stores are lagging" returns their district; the agent cannot widen it.
Yes, through the same completion policy as every other channel — permission, then role, then shift, then site, then training. Because a chat turn has no location, a completion that requires site verification returns a distinct "location can't be verified here" answer and points you at the mobile app, rather than quietly passing or quietly failing.
Yes. Decision history records three kinds of actor — person, system, and AI. An action the agent took is flagged as AI and still names the person it acted for, while automatic platform decisions are recorded as system rather than attributed to whoever was logged in. The history is append-only, CSV-exportable, and retained even if the campaign is deleted.
On-time completion rate, time-to-first-action on a shift, review latency, the share of launches preceded by a workload check, and unassigned-item dwell time. Compare against your pre-agent baseline.
Let's Talk
Since 2008, we've been building the employee platform for the frontline, earning the trust of 2 million+ users and an NPS of 78.
Why Choose Us?
- AI-Ready Platform: One intelligent place 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.
Trusted by Legendary Companies:
Prefer to explore first? Ask AI about Frontline Execution AI →