The Commerce AI Command Deck

Based on all the interest of my recent post from Ag3ntic.ai on “the real reason AI is not transforming eCommerce yet”, I’ve updated and want to re-share a prototype I created of what an agentic commerce dashboard might look like.

It’s not perfect but I hope it can help you visualize even further how things might change.

https://commerce-ai-command-deck.lovable.app

For lack of a better descriptor we can call it the Commerce AI Command Deck.

This is not a product release. It’s a glimpse of what could sit between your business and a swarm of autonomous systems working together in real time.

Here’s what it includes so far.

Agent Registry and Task Monitor

A live panel where you can see every agent in your system, what it’s doing, what context it’s using, and what result it returned. If agents are operating across pricing, merchandising, retention, and support, this would let you track behavior at the execution level.

Vector Store Management

Agents use memory. That memory lives in vector databases. The Command Deck shows what’s being written, when it was last embedded, how fresh it is, and how it’s being used in downstream reasoning. You could refresh, partition, or restrict memory access per agent.

Schema and Context Oversight

If multiple agents are reading from the same data sources, semantic drift becomes a real risk. The dashboard could let you define adapters for Shopify, Postgres, Klaviyo, Segment, and more—then normalize fields into a consistent schema with real-time change tracking.

Prompt Engineering and Simulation Tools

You could version prompts, simulate outcomes, and assign fallback models all in one place. No copy-pasting into playgrounds. You’d be able to preview how agents respond under different data conditions before putting them into production.

Agent Coordination Flows

Instead of standalone bots, agents could hand off tasks to each other. One could rewrite a PDP, another could create images, another could test variants. You’d define the sequence, track the state, and manage shared memory across the chain.

Multi-Model Runtime Controls

You might want different models for different jobs. This dashboard would let you assign GPT-4 for high-stakes work, Claude for summarization, or local models for cost-sensitive flows. You could route based on latency, budget, or reliability in context.

Security, Limits, and Observability

Agents could have scoped permissions. They’d only access what you explicitly allow. Every action would be logged. You’d know what prompt was used, what tool was invoked, and what context was retrieved. No black boxes.

The idea is to create a new kind of operating surface for intelligent collaboration.

You could coordinate memory. You could reason across data. You could build chains of adaptive logic that operate continuously across your business.

It’s still early. But this feels inevitable.

— Dylan Whitman, Ag3ntic.ai