Agentic Commerce: How AI Runs Modern Marketplaces

Most marketplace teams are already using AI—just not in the places that actually change the P&L.

They’ll deploy a chatbot for customer support, maybe a recommendation widget on product pages, and call it “AI in e-commerce.” Meanwhile, the truly expensive work still happens in spreadsheets and inbox threads: onboarding sellers, policing catalog quality, resolving returns disputes, updating inventory, and keeping pricing competitive without blowing margin.

That’s why agentic commerce is the direction worth watching, especially for U.S. retailers and marketplace operators trying to scale digital services. The premise is simple: AI agents don’t just answer questions—they take actions across systems (within guardrails) to keep a marketplace healthy and growing. For a platform business, that’s the difference between “AI as a feature” and AI as operating capacity.

This post is part of our AI in Retail & E-Commerce series, where we focus on practical uses of AI for personalization, demand forecasting, dynamic pricing, and inventory management. Agentic commerce ties all of those together by turning insights into execution.

What “agentic commerce” actually means (and why it matters)

Agentic commerce is when AI systems can plan and execute multi-step commerce workflows—across tools—without requiring a human to push every button.

Traditional automation is rigid: “If X, then Y.” Agentic workflows are closer to: “Here’s the goal, here are the policies, here are the tools—go do it and report back.” In a marketplace context, that might mean an AI agent can:

• Detect a seller’s catalog has missing attributes and fix listings in bulk
• Identify policy violations (restricted items, inaccurate claims) and trigger enforcement steps
• Propose a pricing update based on competitor signals and margin rules
• Forecast inventory risk and open replenishment tickets
• Draft seller outreach, then route it for approval

This matters because marketplaces are operations-heavy businesses. If you run a third-party marketplace or a dropship network, growth isn’t limited by pageviews—it’s limited by how fast your team can maintain quality and trust while adding sellers and SKUs.

A concise way to say it:

Marketplaces don’t scale on assortment alone; they scale on operational control. Agentic commerce is AI applied to that control layer.

Where Mirakl fits in the U.S. digital economy

Mirakl is best known for helping enterprises launch and run online marketplaces—turning a retailer or B2B distributor into a platform that can host third-party sellers and expanded assortment.

In the United States, that’s become a core digital strategy for large retailers, grocers, and B2B suppliers. The logic is familiar:

• Expand selection without owning inventory
• Add high-margin services (seller tools, advertising, fulfillment programs)
• Turn the marketplace into a growth engine that can fund better customer experience

Agentic commerce aligns with this trajectory because it treats AI as part of the marketplace operating system, not just a front-end enhancement. For U.S. businesses building digital services—especially SaaS-like seller portals, ad products, and fulfillment workflows—agents can reduce the cost and time to run the platform.

In practice, the biggest win isn’t “AI wrote a product description.” It’s “AI kept our catalog clean enough that conversion didn’t tank after we added 1 million SKUs.”

The five workflows where agentic commerce pays off first

If you’re deciding where to invest, start where the work is repetitive, high-volume, and tied to revenue outcomes. These are the areas I’ve seen deliver the fastest value in AI-powered marketplaces.

1) Seller onboarding and activation

Fast onboarding is pointless if sellers don’t activate. Activation means they publish compliant listings, maintain inventory accuracy, ship on time, and earn good customer feedback.

An agentic approach can:

• Check seller documents, tax forms, and policy attestations
• Detect missing data and request it in a structured way
• Recommend the minimum set of listings needed to start selling
• Route edge cases to humans with a clear summary (“fails OFAC check,” “missing W-9,” “category requires additional certification”)

Lead impact: shorter time-to-first-sale and fewer onboarding tickets. For U.S. marketplace operators trying to grow quickly without staffing up support teams, this is usually the first place to deploy.

2) Catalog quality at scale

Catalog issues kill performance quietly. Attribute gaps, duplicate listings, mismatched variants, and incorrect category mapping show up as lower conversion and higher return rates.

A commerce agent can continuously:

• Normalize titles and attributes against a taxonomy
• Detect likely duplicates and propose merges
• Flag claims that violate policy (medical, safety, restricted items)
• Identify “low-quality clusters” (brands or sellers with unusually high defect rates)

Strong stance: If your marketplace has more than ~250,000 SKUs, catalog quality can’t be a quarterly cleanup project. It has to be an always-on system.

3) Dynamic pricing that respects margin and brand

Dynamic pricing has existed for years, but many teams still treat it as a rules engine. Agentic pricing adds decisioning that can consider context and execute actions responsibly.

A pricing agent can:

• Monitor competitor price moves and stock status
• Recommend price changes constrained by margin floors, MAP policies, and brand rules
• Coordinate promotions with inventory levels and fulfillment capacity

This is where AI in retail becomes operationally meaningful: pricing changes aren’t a dashboard recommendation; they’re actions logged, reviewed, and rolled out with safeguards.

4) Inventory risk and fulfillment coordination

Marketplace inventory is messy because you don’t control it. But customers still blame you.

An agent can:

• Forecast stockout risk using sales velocity, seasonality, and seller performance
• Detect inventory drift (listed inventory vs. actual shipped capacity)
• Trigger workflows: “pause ads,” “extend handling time,” “request replenishment,” or “limit order quantity”

This ties directly to the broader series themes: demand forecasting, inventory management, and customer experience are linked. Agents help convert forecasts into operational changes before customers feel the problem.

5) Returns, disputes, and trust & safety

Returns and disputes are where margin goes to die. It’s also where platform trust is won or lost.

Agentic systems can:

• Summarize order history, messages, and tracking into a single case narrative
• Detect likely fraud patterns (repeat claim behavior, mismatch between item value and claim type)
• Propose resolutions consistent with policy and past precedent
• Route only ambiguous cases to humans

The operational benefit is obvious. The strategic benefit is bigger: consistent enforcement and faster resolutions improve customer trust—critical for marketplace growth.

How to build agentic commerce responsibly (without chaos)

The biggest mistake is giving an agent too much power too early. The goal is controlled autonomy, not a runaway automation layer.

Here’s a practical blueprint that works for most U.S. digital commerce teams.

Design around “guardrails first”

Your agent needs explicit constraints:

• Policy guardrails: restricted categories, claims language, MAP rules
• Financial guardrails: margin floors, refund limits, promo caps
• Operational guardrails: rate limits, approval thresholds, escalation rules

A good internal mantra is:

Agents should be free to act, but never free to invent policy.

Start with “human-in-the-loop,” then earn autonomy

Roll out in phases:

1. Draft mode: agent recommends actions; humans execute
2. Assisted mode: agent executes low-risk actions; humans approve higher-risk ones
3. Autonomous mode: agent executes within narrow scopes with monitoring and rollback

Most teams jump from phase 1 to phase 3 and get burned. Phase 2 is where you build trust and tune the system.

Measure the right outcomes (not vanity metrics)

Track metrics that map to marketplace health:

• Time-to-first-sale (seller activation)
• Listing defect rate and attribute completeness
• Return rate and dispute cycle time
• On-time shipment and cancellation rate
• Contribution margin impact from pricing and promo actions

If your “AI success” dashboard doesn’t include margin, defect rate, and cycle time, it’s not telling the truth.

People also ask: what’s the difference between agentic commerce and personalization?

Personalization decides what to show. Agentic commerce decides what to do.

Personalization is valuable—recommendations, ranking, and tailored offers can lift conversion. But agentic commerce targets the workflows behind the scenes that determine whether the marketplace is trustworthy and efficient.

A practical way to separate them:

• Personalization: “Show this shopper running shoes in size 10.”
• Agentic commerce: “Fix the shoe listings missing size data, pause unreliable sellers, update pricing based on stock, and reduce returns by improving fit guidance.”

They work best together. Personalization increases demand; agentic operations keep supply and quality stable.

What to do next if you run a marketplace (or want to)

If you’re leading e-commerce, product, or marketplace operations in the U.S., agentic commerce should change your roadmap for 2026. Not because it’s trendy, but because it directly attacks the limiting factor: operational overhead.

Here’s a focused next-step plan you can execute in a quarter:

1. Pick one workflow with clear ROI (catalog fixes, seller onboarding, returns triage)
2. Define guardrails and an approval model (what can auto-execute vs. what needs review)
3. Instrument the pipeline so every action is logged, explainable, and reversible
4. Roll out to a subset of sellers or categories, then expand based on measured outcomes

The real opportunity is bigger than a single workflow. If you treat your marketplace like a digital service business—software + operations + trust—agents become a scalable labor layer that helps you grow without matching headcount to SKU count.

Where does this go next? The marketplaces that win won’t just have more sellers. They’ll have faster feedback loops—and AI agents will be a big part of how those loops run.