Manual merchandising rhythms cannot keep up with live commerce signals. An agentic control room lets AI assistants watch sell-through, margins, and demand shocks around the clock while merchandisers approve strategic moves.
This playbook explains how to build that control room in three sprints. You will stream trusted data, define price and inventory agents, and publish a dashboard that shows every change with rationale so finance, ops, and growth stay aligned.
Key outcomes from this playbook
Agents balance price, discount, and inventory exposure before humans wake up.
Real-time markdown and bundle automation reduces stranded inventory.
Control room publishes a human-readable log with every approved action and its effect.
Agentic blueprint
Phase 1: Consolidate live signals
A control room is only as good as its telemetry. Bring pricing, inventory, campaign, and customer intent data into one warehouse with near real-time refresh.
- Stream POS and e-commerce orders into the same dataset with channel tags.
- Append demand signals from search volume, waitlists, and field sales notes.
- Normalize product attributes so agents can reason about kitted inventory and alternates.
Phase 2: Draft agent playbooks
Give each agent a precise job: price tuner, bundle composer, inventory balancer, or promo forecaster.
- Define the levers each agent can touch (price window, promo depth, stock reallocation).
- Encode hard guardrails like minimum margin, compliance rules, and sales tax boundaries.
- Establish approval rules: auto-run below threshold, require human consent above it.
Phase 3: Launch the control room
Build a shared dashboard plus notifications that explain what the agents saw, decided, and expect next.
- Ship a Looker or Mode board with margin, sell-through, demand, and pending actions.
- Send Slack or Teams digests at 9 a.m. local time with the top five recommendations.
- Log every change with SKU, timestamp, agent, and reason code for audit readiness.
Rapid launch checklist
Sync ERP, OMS, and marketing platforms into a single warehouse view with hourly refresh.
Most teams begin with dynamic bundles and automated low-stock alerts before touching price.
Mock the control room layout so merchandisers agree on metrics, thresholds, and filters.
Agents propose actions for two weeks. Humans approve or reject with comments to train the loop.
Metrics to watch
Percent of agent recommendations approved. Target 60 percent once trust forms.
Track revenue and profit delta for each accepted change to prove ROI.
Measure days of cover variance across regions. Agents should tighten the spread.
Monitor how many notifications merchandisers snooze or mute to keep the signal high.
Suggested toolkit
Snowflake or BigQuery with dbt models to standardize SKU level facts.
LangGraph or CrewAI to coordinate multiple agents with shared memory.
Looker, Hex, or Metabase powering the control room board.
PagerDuty, Slack workflows, or custom webhooks triggered by the orchestration layer.
Quick answers
How do we stop agents from racing to the bottom on price?
Set minimum margin thresholds, only allow price changes within preapproved windows, and require finance approval for anything above a five percent move.
Can the control room support omnichannel inventory?
Yes. Stream store inventory, e-commerce availability, and marketplace stock into the same model so the agent can reallocate or pause campaigns per channel.
What team manages the control room?
Merchandising owns prioritization, revenue operations owns orchestration, and engineering ensures data quality. Meet weekly to review logs and tune guardrails.
Need a builder for this playbook?
These playbooks are designed for teams that want clarity fast. If you prefer to skip the trial and error, I can architect the agentic workflow, integrate your tool stack, and train your team on continuous improvement.