AITraining2U Demo · KitaMart — a fictional retail brand
Business Workflow Platform · Blueprint v1 · 10 Jul 2026

Run the whole convenience network as one ranked decision list, every morning at 06:00.

Seven specialist AI agents read your sales tills (POS), central-kitchen production, chiller telemetry, rosters and loyalty data overnight. One Chief-of-Staff orchestrator reconciles them into 3–7 RM-quantified decisions your leadership team ratifies over coffee. Agents draft — humans approve. Nothing touches a live price, a PO, or the FPC production plan without a human click.

7+1
Specialist agents + Chief of Staff
612
Outlets modelled — KitaMart · QikStop · TravelMart · Kafe Kita*
06:00
Daily Decision List on the CEO's phone
RM 14.2M
Modelled annualised decision swing*

*All figures on this page are illustrative mock data, built to demonstrate the operating model — swap in live feeds and the platform renders your actual business.

1 Tensions 2 Agent team 3 Architecture 4 Daily cascade 5 Live demo 6 Data & memory 7 Rollout Compliance
1 Map the operating tensions

Every convenience network runs on seven daily trade-offs. Today they're resolved by whoever pulled the dashboard last.

The starting point isn't technology — it's naming the recurring decisions the business makes every single day. Each tension below becomes the charter of exactly one agent. If a decision doesn't map to a tension, no agent owns it and it stays human-only.

Margin vs Volume

What should a Milo kotak or onigiri sell for at a KLCC concourse store versus a neighbourhood shoplot beside a discount minimart? One national price sheet leaves money on the table both ways.

→ Pricing & Demand Agent
Production vs Demand

The central kitchen (the FPC) cooks tonight what 600+ stores must sell tomorrow. Over-produce and fresh food expires in the chiller; under-produce and the lunch crowd walks out empty-handed. This is the highest-leverage call in the company — made nightly.

→ Food Production Agent
Stockout vs Spoilage

Ready-to-eat has a 24–48h shelf life. Allocating 40 onigiri to a store that sells 25 burns margin; allocating 25 to a store that sells 40 burns customers.

→ Inventory & Allocation Agent
Roster vs Wage cost

Transit and airport stores peak with train schedules and flight banks, not office hours. Flat rosters overstaff dead hours and understaff the rush.

→ Staffing & Rostering Agent
Maintenance vs Uptime

A drifting chiller isn't just an energy bill — it's halal cold-chain integrity and a bin full of expired bento. Service too early wastes money; too late loses a weekend of fresh sales.

→ Facilities & Energy Agent
Reach vs Spend

App pushes and combo promos only work when they match what's actually priced, stocked and staffed this week — not last month's campaign calendar.

→ Marketing & Loyalty Agent
Scale vs Cull

Across KitaMart, QikStop-conversion, TravelMart airport stores and Kafe Kita counters: which formats and sites get capital, which get a refresh, which get closure diligence?

→ Portfolio & Performance Agent
2 Charter the agent team

Seven specialists, one Chief of Staff. Each has a persona, a bounded authority, and a contract for what it emits.

These charters paste directly into Claude projects or n8n AI-agent nodes as system prompts. The critical design rule: every agent's authority stops at a draft. POs, price changes, production runs, rosters and promos all wait for a human click. Compliance rules are baked into each persona, not bolted on afterwards.

Pricing & Demand AgentDrafts only
Reads
POS velocity (SKU × store), nearby competitor prices, festive calendar (Raya · CNY · Deepavali · school holidays · paydays), weather & haze index.
Hands to
The Production, Inventory, Marketing and Portfolio agents — its 14-day forecast is the number everyone else plans against.
"For the next 14 days, here's what each SKU should sell for at each store, and the demand curve every other agent plans against."
Food Production AgentDrafts only
Reads
The Pricing agent's forecast, central-kitchen line capacity & yield (onigiri, sandwich, bento, bakery, Kafe Kita supplies), ingredient stock & lead times, waste logs per store.
Hands to
The Inventory agent (what's available to allocate tomorrow) and the Portfolio agent (waste vs sell-through per line).
"Here's tonight's production run per line — sized so tomorrow's fresh food sells through above 92% with under 5% waste."
Inventory & Allocation AgentDrafts POs
Reads
The Pricing forecast, the Production run, stock-on-hand per store, vendor lead times, spoilage & dead-stock, min/max policy. Listens to the Facilities agent — never routes fresh food to a store with a flagged chiller.
Hands to
The Portfolio agent. Escalates vendor issues to humans.
"Here are today's ambient purchase orders and the per-store fresh allocation, with stockout and spoilage risk quantified on each line."
Staffing & Rostering AgentDrafts rosters
Reads
HR-system rosters, footfall curves per store, LRT/MRT schedules, KLIA flight banks (for TravelMart airport stores), leave & OT balances, Employment Act limits.
Hands to
The Portfolio agent (labour cost per store) and the Chief of Staff (coverage gaps needing human calls).
"Here's next week's roster per store, shaped to the traffic curve — with OT kept inside policy and gaps flagged for area managers."
Facilities & Energy AgentFlags only
Reads
IoT telemetry — chiller/freezer temperature, compressor current, door-open counts, HVAC, coffee-machine cycles — plus maintenance logs and TNB tariff windows.
Hands to
The Inventory agent (chiller warnings gate fresh deliveries) and the Portfolio agent (energy cost per store).
"Here's what's likely to fail this week, when to service it cheapest, and which stores can dodge peak-tariff charges tomorrow."
Marketing & Loyalty AgentDrafts only
Reads
The Pricing forecast (what to push), loyalty app segments & redemption data, festive calendar, which stores actually have the stock and staff to serve a promo.
Hands to
The Portfolio agent — every campaign gets a measured lift, or it doesn't run again.
"Here are ready-to-schedule app pushes, combos and POSM briefs — matched to this week's pricing, stock and staffing reality."
Portfolio & Performance AgentRecommends
Reads
All six agents + per-store P&L, basket, footfall, format economics (KitaMart vs QikStop-conversion vs TravelMart vs Kafe Kita attach rate).
Hands to
The Chief of Staff — with the specific playbook proven on similar stores attached to every recommendation.
"Winners to replicate, laggards to fix or exit — and the action that already worked on the 8 most similar stores."
Chief of Staff OrchestratorRanks & routes
Reads
Every agent's overnight output + the Outcomes memory (what leadership approved before, and what actually happened).
Emits
The Daily Decision List — 3–7 ranked, RM-quantified decisions at 06:00, each traced to its source agents, each with Approve / Defer / Escalate buttons. Conflicts between agents (e.g. Marketing wants to promote what Inventory can't stock) get resolved or surfaced — never silently dropped.
"Don't read seven reports. Ratify five decisions. I'll tell you tomorrow whether yesterday's calls made the money we modelled."
3 Wire the architecture

Three layers: signals in, agents reason, one orchestrator out. Memory makes it compound.

Every arrow below is an automated workflow with a defined hand-off contract — no black boxes, and deliberately tech-neutral: the same design runs on any orchestration stack. The signal layer is read-only against systems you already run. The write-path back into POS, ERP and rostering only opens after a human approval, and every recommendation is logged for Bursa-grade auditability.

LAYER 1 — SIGNALS (READ-ONLY) POS · 612 outletsBaskets · velocity Central kitchenLines · yield · waste IoT telemetryChillers · HVAC · kWh HR rostersShifts · OT · leave Loyalty app / CRMSegments · redemptions ExternalWeather · flights · KPDN LAYER 2 — 7 SPECIALIST AGENTS Pricingprices & demand Productioncentral kitchen Inventorystock & allocation Staffingrosters & shifts Facilitieschillers & energy Marketingpromos & loyalty Portfoliostore performance Facilities→Inventory: chiller flagged ⇒ divert fresh delivery LAYER 3 — ORCHESTRATION The Chief of Staff Reconciles 7 agents → 1 ranked list · resolves conflicts by RM-impact CEO / COO / Area GMs · 06:00 MYT Approve · Defer · Escalate — from the phone, before the first coffee SHARED MEMORY ↔ ALL AGENTS Decisions · every list entry, immutable Outcomes · what actually happened Playbooks · only proven actions fire Context · leadership risk appetite Phase 4+: vector DB, per-agent namespaces, MCP retrieval tools APPROVE → LEARN LOOP Click Approve → the action executes → outcome measured at D+1 / D+7 → stamped into Outcomes nightly → agent forecast error scored → playbooks refreshed → tomorrow's list is sharper
Signals → specialists → one list → human click → measured outcome. The orange arrow is the money link: no analyst juggling spreadsheets at 3am catches a drifting chiller before the fresh-food truck loads — the cascade does.
4 Schedule the overnight cascade

While the network sleeps, the agents run in strict order — because each one plans against the previous one's output.

Sequence matters more than intelligence. The FPC can't size tonight's run before the forecast locks; allocation can't route fresh food before the run is known and the chillers are checked. Each step is a scheduled, automated workflow with a hard hand-off contract.

22:00
Pricing & Demand Agent
Refreshes the 14-day demand forecast per SKU × store from today's closing POS, weather and calendar. This is the number everyone downstream plans against.
23:00
Food Production Agent
Sizes tonight's production run per line — onigiri, sandwich, bento, bakery, Kafe Kita supplies — against tomorrow's forecast, line capacity and ingredient stock. Draft goes to the central-kitchen production manager for the overnight shift.
03:00
Facilities & Energy Agent
Sweeps overnight chiller/HVAC telemetry across all outlets. Flags failure risk and peak-tariff opportunities. Chiller flags are published before allocation locks.
04:00
Inventory & Allocation Agent
Drafts ambient purchase orders and the per-store fresh allocation from the central-kitchen run — automatically diverting fresh food away from any store the Facilities agent flagged, toward its nearest healthy neighbours.
04:30
Staffing & Rostering Agent
Checks today's rosters against the demand curve and flight/train schedules; drafts swaps and OT requests inside Employment Act limits.
05:00
Marketing + Portfolio Agents
Marketing drafts promos that match what's actually priced, stocked and staffed. Portfolio re-tiers all 612 outlets (Over / On-target / Under) and attaches the proven playbook to every laggard.
05:30
Chief of Staff
Reconciles all seven outputs, kills conflicts, ranks by RM-impact × confidence, checks the Outcomes memory so it never re-pitches what leadership already declined.
06:00
→ Leadership
The Daily Decision List lands — email + app. 3–7 decisions, each RM-quantified, each with Approve / Defer / Escalate. Fifteen minutes over coffee, and the network is steered for the day.
02:00
+1
Nightly learning loop
Yesterday's approved decisions are scored: modelled RM vs actual RM. Outcomes stamp into memory, agent errors are fed back, playbooks refresh. This loop is the difference between a chatbot and a system that compounds.
5 Play it — the morning briefing, as a game

Chat with your Chief of Staff. Make the calls. Get scored when the outcomes land tomorrow.

This is a closed-loop simulation: weather shifts, chillers drift, vendors slip, promos lift — and the subagents (code in retail-sim.js, personas in retail-agents.md) draft each morning's decisions from that updating data. The Chief of Staff bot briefs you exactly like the 06:00 digest would — one decision at a time. Approve, defer, escalate, or ask why. End the day, the world moves, and your calls get scored: capture the upside, avert the risks, climb from Trainee Manager to CEO Material. Every panel in the ops room below updates as you play.

CS
Chief of Staff
KitaMart Ops · on duty
Trainee Manager
0 pts
Day 0 · streak 0 · make your first calls
How scoring works
✅ Approved gain lands → +1 pt / RM 1k actual
🛡 Risk averted → +1 pt / RM 1k averted
👤 Escalation routed → +5 pts
➖ Upside missed (defer/ignore) → −½ pt / RM 1k
❌ Ignored risk realises → −1 pt / RM 1k lost

Daily grade = RM captured ÷ RM available (A ≥ 80%, B ≥ 60%, C ≥ 40%). Grade B or better keeps your 🔥 streak alive. Ranks: Trainee → Store Manager (100) → Area Manager (250) → Ops Director (500) → COO (900) → CEO Material (1500).

Blindly approving everything won't max your score — some risks are theatre, some upside is real. Read the why.
The ops room — updates live as you play

24-hour P&L scope · selected day

Approve upside / risk if ignored

Network vitals — the signals the agents read

Network tier snapshot · 612 outlets

Over-performing
On-target
Under-performing
Open the outlet scatter — where the Portfolio Agent sees the opportunities
Over-performing — replicate On-target — protect Under-performing — fix or cull
Each dot is one outlet from the mock network (48 shown of 612). X-axis: 30-day average daily footfall. Y-axis: 30-day average basket (RM). Quadrant labels are PPA's standing recommendation per zone. Hover any dot for outlet detail.

Ranked decisions

The overnight balancing conversation

This is the negotiation that produced the decision list above — reconstructed nightly from the live world state. Watch for warnings (a constraint published before it bites), blocks (the Chief of Staff killing a conflicting proposal) and the final route to the CEO. No human juggles these hand-offs at 4am; the cascade does.

Overnight outcomes · the learning loop

Forecast gap — modelled vs actual on approved decisions

The one governing metric: how far actuals land from the agents' models, per scoring day. Gap trending down = the system is learning (each scored outcome nudges that agent's accuracy up). Gap flat = stop adding agents and fix the one you have.
6 Lay the data & memory foundation

Operational data the agents read daily — and the memory that makes them smarter every week.

Start pragmatic: nightly extracts into a shared store the agents can read (warehouse tables, or even governed Sheets in the pilot). The memory layer is four small databases. The vector/MCP retrieval backbone is deliberately deferred to Phase 4 — don't build it before there are memories worth retrieving.

Layer A — Operational data (refreshed nightly)

Read-only extracts from systems KitaMart already runs. No rip-and-replace.

FeedSourceRead by
salesPOS — SKU × outlet × hourPricing · Production · Inventory · Portfolio
fpcCentral kitchen (FPC) — lines, yield, wasteProduction · Inventory · Portfolio
stockPOS / WMS — on-hand, expiryInventory · Facilities
iotChiller / HVAC / energy sensorsFacilities → gates Inventory
rosterHR system — shifts, overtime, leaveStaffing · Portfolio
loyaltyApp / CRM — segments, redemptionsMarketing · Portfolio
externalWeather · flight banks · competitor · KPDN circularsPricing · Staffing · Marketing
pnlFinance — per-outlet P&LPortfolio · Chief of Staff

Layer B — Memory (the learning substrate)

Four databases. Small, boring, and the entire reason the system compounds.

DatabaseHoldsWhy it matters
DecisionsEvery decision-list entry, immutableAudit trail (Bursa-grade) + never re-pitch a declined call
OutcomesApproved decisions + measured D+1/D+7 resultThe learning loop — modelled vs actual RM
PlaybooksActions proven to work, by store clusterThe Portfolio agent only recommends what has already worked on similar outlets
ContextLeadership risk appetite, learned from clicksThe Chief of Staff calibrates how aggressive tomorrow's list should be
The write-back loop, end to end: agent predicts → Chief of Staff ranks → human approves → n8n executes → outcome measured → memory stamped → agent scored → playbook refreshed. predict → ratify → execute → measure → learn → (repeat, sharper)
Phase 4 upgrade (only when ~500 decisions are logged): add a vector DB with one namespace per agent + hybrid retrieval (dense for meaning, sparse for exact SKU/outlet IDs), exposed to agents via MCP tools — search_memory · get_decision_history · get_playbook · write_note. It's the highest-maintenance piece; earn it before you build it.
7 Roll out in phases — one trusted agent at a time

Never build the swarm at once. Each phase has a gate it must pass before the next begins.

0
Weeks 1–2

Shadow run

Ops leadership hand-writes the 5-item decision list for two weeks using existing reports. Proves the decisions are worth automating — before any build.

Gate: leadership agrees the list captures the real daily calls.
1
Weeks 3–8

Production + Inventory agents on fresh food

30 pilot Klang Valley outlets. The FPC production ↔ allocation loop is the clearest right-answer and the fastest payback: spoilage and stockout are both measurable within days.

Gate: fresh sell-through ↑ and waste ↓ vs the 30-store control group.
2
Months 2–3

Pricing agent + Chief of Staff digest

Add pricing/demand and a minimal Chief of Staff merging both streams into one 06:00 digest for the pilot region. First moment it feels like the operating model.

Gate: ≥70% of digest items approved without edits for 3 straight weeks.
3
Months 3–5

Facilities · Staffing · Marketing + memory

One at a time, network-wide. IoT retrofits on chillers where telemetry is missing. Nightly outcome-stamping goes live — the system starts learning.

Gate: the Facilities agent catches a real failure before it happens; roster fill-rate ↑.
4
Month 5+

Portfolio agent + vector memory

Portfolio agent joins with full P&L access; quarterly format reviews (KitaMart / QikStop-conversion / TravelMart / Kafe Kita attach) run on evidence, not anecdote. Vector/MCP retrieval added once ~500 decisions are logged.

Gate: the governing metric (below) is trending to zero.

The one metric that governs everything

Modelled RM-impact vs actual RM-impact on approved decisions. If the gap closes month over month, the system is learning — keep adding agents. If it doesn't, stop adding agents and fix the one you have. This single number is also the board's answer to "is the AI actually making us money?"

Compliance is baked into the personas

Malaysia-specific guardrails, enforced at the agent level — not reviewed after the fact.

KPDN

Price display & labelling rules; festive price-control lists honoured automatically in every price draft.

enforced in the Pricing agent
JAKIM Halal

FPC halal traceability + cold-chain integrity. A flagged chiller isn't just spoilage risk — it's a certification risk, which is why the Facilities agent gates every fresh delivery.

enforced in the Production · Inventory · Facilities agents
MOH / Food Safety

Expiry discipline, temperature logs and recall readiness on all ready-to-eat food lines — the telemetry trail doubles as the compliance record.

enforced in the Production · Facilities agents
Employment Act

OT ceilings, rest-day and shift-gap rules are hard constraints in every roster draft — the agent cannot propose an illegal roster.

enforced in the Staffing agent
PDPA

Loyalty segments are used in aggregate; no individual customer data ever enters a prompt. Consent flags respected on every push.

enforced in the Marketing agent
Bursa governance

Every recommendation, click and outcome is immutably logged — an audit trail a listed company can show its auditors and its board.

enforced in the Chief of Staff · memory layer

What to do Monday

1 — Nominate the pilot: 30 Klang Valley outlets + the FPC fresh-food loop.  2 — Run the two-week shadow list by hand.  3 — Let us build the Production + Inventory agents as the first trusted pair. Everything else waits until they've earned it.