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.
*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.
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.
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 AgentThe 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 AgentReady-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 AgentTransit and airport stores peak with train schedules and flight banks, not office hours. Flat rosters overstaff dead hours and understaff the rush.
→ Staffing & Rostering AgentA 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 AgentApp 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 AgentAcross 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 AgentThese 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.
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.
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.
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.
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.
Read-only extracts from systems KitaMart already runs. No rip-and-replace.
| Feed | Source | Read by |
|---|---|---|
| sales | POS — SKU × outlet × hour | Pricing · Production · Inventory · Portfolio |
| fpc | Central kitchen (FPC) — lines, yield, waste | Production · Inventory · Portfolio |
| stock | POS / WMS — on-hand, expiry | Inventory · Facilities |
| iot | Chiller / HVAC / energy sensors | Facilities → gates Inventory |
| roster | HR system — shifts, overtime, leave | Staffing · Portfolio |
| loyalty | App / CRM — segments, redemptions | Marketing · Portfolio |
| external | Weather · flight banks · competitor · KPDN circulars | Pricing · Staffing · Marketing |
| pnl | Finance — per-outlet P&L | Portfolio · Chief of Staff |
Four databases. Small, boring, and the entire reason the system compounds.
| Database | Holds | Why it matters |
|---|---|---|
| Decisions | Every decision-list entry, immutable | Audit trail (Bursa-grade) + never re-pitch a declined call |
| Outcomes | Approved decisions + measured D+1/D+7 result | The learning loop — modelled vs actual RM |
| Playbooks | Actions proven to work, by store cluster | The Portfolio agent only recommends what has already worked on similar outlets |
| Context | Leadership risk appetite, learned from clicks | The Chief of Staff calibrates how aggressive tomorrow's list should be |
search_memory · get_decision_history · get_playbook · write_note. It's the highest-maintenance piece; earn it before you build it.
Ops leadership hand-writes the 5-item decision list for two weeks using existing reports. Proves the decisions are worth automating — before any build.
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.
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.
One at a time, network-wide. IoT retrofits on chillers where telemetry is missing. Nightly outcome-stamping goes live — the system starts learning.
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.
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?"
Price display & labelling rules; festive price-control lists honoured automatically in every price draft.
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.
Expiry discipline, temperature logs and recall readiness on all ready-to-eat food lines — the telemetry trail doubles as the compliance record.
OT ceilings, rest-day and shift-gap rules are hard constraints in every roster draft — the agent cannot propose an illegal roster.
Loyalty segments are used in aggregate; no individual customer data ever enters a prompt. Consent flags respected on every push.
Every recommendation, click and outcome is immutably logged — an audit trail a listed company can show its auditors and its board.
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.