Agentic AI in Retail: Lessons from Levi’s + Microsoft

Retailers love to talk about “AI.” Most are still using it like a fancy search bar.

Levi Strauss is signalling a different direction: agentic AI built around a single “super agent” inside Microsoft Teams, backed by Azure, and supported by specialised sub‑agents across IT, HR, operations, and more. The point isn’t a chatbot that answers questions. The point is work that gets done—faster, more consistently, and across channels.

For Irish retailers and e-commerce teams, this matters right now. December is when you feel every crack in the operation: customer queries spike, returns pile up, stock gets messy, and promotions can go sideways in hours. Agentic AI is basically a pressure-tested way to reduce the chaos—and to improve customer experience without hiring a small army.

What Levi’s partnership tells us about where retail AI is heading

Agentic AI is moving from “assistive” to “operational.” That’s the shift Levi’s is betting on.

Levi’s plans an early 2026 rollout of an integrated platform centred on one super agent embedded in Teams. It will orchestrate multiple specialised AI agents (think: a returns agent, an inventory agent, a staff scheduling agent) and act as an intelligent intermediary. In plain terms: employees stay in the tools they already use, and AI coordinates the busywork.

This matters because most AI projects fail at the last metre—not because the model is bad, but because nobody adopts it. If your AI lives in a separate portal, staff won’t open it when the queue is out the door. Putting it in Teams is a smart adoption move.

Two more details worth noticing:

• Levi’s frames this as part of becoming a direct-to-consumer first retailer. Agentic AI is being positioned as the operating layer for DTC speed.
• They’re pairing corporate automation with frontline tools: Outfitting (personalised styling) and Stitch (an in-store AI assistant being expanded to 60 US locations after a pilot).

That “both sides of the house” approach is what makes it credible.

Agentic AI, explained without the hype

Agentic AI in retail is simple: software that can plan and execute multi-step tasks across systems, with guardrails.

A standard chatbot answers: “Where’s my order?” An agentic system can:

1. Check the order management system.
2. Query carrier status.
3. Apply the right policy (refund vs replacement vs reship).
4. Create the ticket and send the customer an update.
5. Flag a pattern if a courier lane is failing.

That’s why Levi’s “super agent” concept is interesting. It suggests an architecture where:

• Sub-agents specialise (HR onboarding, store ops, IT helpdesk, product content, merchandising insights).
• The super agent routes work to the right sub-agent and brings back a single, usable answer—or completes the task.

A useful rule: if your AI can’t take an action (with approval), you don’t have agentic AI—you have an FAQ.

For omnichannel retailers, action is the whole point. Customers don’t experience “departments.” They experience friction.

Where agentic AI actually improves omnichannel customer experience

The fastest CX wins come from fixing the invisible operational stuff customers constantly bump into.

Faster answers are nice. Fewer failures is better.

Most retailers focus on response time. That’s easy to measure.

But the real loyalty gains come when you reduce:

• overselling an item that isn’t actually available
• delayed dispatch due to pick/pack exceptions
• inconsistent promo logic between online and store
• returns stuck in limbo because the refund workflow is fragmented

Agentic AI helps because it can connect the dots across systems and trigger the next step, not just report what happened.

Personalisation that doesn’t break trust

Levi’s launched a personalised styling feature in its app (Outfitting). Personalisation in e-commerce works when it’s:

• contextual (season, occasion, weather if you have consented data)
• inventory-aware (no recommending what’s gone)
• transparent (why something is recommended)

I’m opinionated on this: if personalisation feels like surveillance, it backfires. The best retailers treat personalisation as helpful merchandising, not behavioural manipulation.

Store teams get real-time help (and that changes the vibe)

Levi’s “Stitch” assistant for store employees is the kind of tool that quietly upgrades the whole in-store experience. When staff can instantly get:

• product knowledge (fits, fabrics, care)
• availability across nearby stores
• policy guidance for exchanges
• clienteling prompts (without being creepy)

…they stop “going to check” for five minutes and start actually serving.

For Irish retailers, this is especially relevant because many stores run lean. A capable staff assistant can be the difference between a confident recommendation and a lost sale.

What Irish retailers can copy (without Levi’s budget)

You don’t need a global transformation programme to get value from agentic AI. You need a tight scope, clean data paths, and strong guardrails.

1) Start with a workflow that already hurts

Pick one workflow that’s:

• high volume
• repetitive
• cross-system
• customer-impacting

Good candidates in Irish retail and e-commerce:

• “Where is my order?” plus proactive delay notifications
• returns triage and refund status updates
• product content enrichment (attributes, sizing info, fit notes)
• click-and-collect exception handling
• store-to-online stock checks and transfers

The goal is to reduce operational drag, not to impress anyone with AI vocabulary.

2) Use a “super agent” pattern even if you only have two agents

Levi’s super agent is an orchestration layer. You can mimic that architecture in a lightweight way:

• Agent A: Customer service actions (order lookup, refund initiation, reship requests)
• Agent B: Ops intelligence (inventory anomalies, late carrier lanes, promo conflicts)
• Orchestrator: decides which agent to call, logs decisions, asks for approval when needed

Even a simple orchestrator improves reliability because it standardises how work gets routed.

3) Put the AI where staff already work

Levi’s chose Teams. Many Irish retailers live in:

• Teams
• Slack
• a helpdesk tool
• the POS back office

If your AI requires a new login, adoption drops. If it shows up inside the daily workflow, it gets used.

4) Add guardrails before you add features

Agentic AI should earn trust. Guardrails do that.

Practical guardrails I recommend:

• permissioning by role (store associate vs manager vs head office)
• approval gates for refunds above a threshold
• policy-as-code rules (e.g., “no refunds after X days” unless manager override)
• full audit logs of actions taken and data accessed
• fallback paths to humans when confidence is low

This is how you avoid “AI did something weird” becoming an internal meme.

The business case: what to measure in 90 days

If you’re using AI in retail and e-commerce to generate leads (and to justify budget), you need metrics that executives and operators both respect.

Here’s a clean 90-day measurement set:

Customer experience metrics

• contact rate per 1,000 orders (should fall)
• first contact resolution (should rise)
• delivery promise accuracy (should rise)
• returns cycle time (should fall)

Operational metrics

• average handling time for common tickets
• exception backlog (pick/pack issues, C&C failures)
• time-to-publish product pages (especially seasonal drops)

Commercial metrics (tie to revenue carefully)

• conversion rate lift on personalised recommendations
• basket size changes for styled bundles
• reduced markdown loss due to better allocation decisions

A stance I’ll defend: don’t promise revenue first. Promise reliability first. Revenue follows when the customer journey stops breaking.

People also ask: quick answers on agentic AI for retail

Is agentic AI just chatbots with extra steps?

No. A chatbot talks. Agentic AI executes tasks across systems, often with approvals and audit trails.

Will agentic AI replace store staff or customer service teams?

It usually replaces the worst parts of the job: repetitive lookups, copy/paste work, policy hunting. The teams still matter—especially when emotion, judgement, or exceptions are involved.

What data do you need for agentic AI?

You need reliable access to: product catalogue, inventory, orders, returns, customer profiles (with consent), policies, and operational status signals (carriers, store hours, staffing).

What’s the biggest risk?

Poor controls. If the AI can take actions without limits, you’ll see refund leakage, inconsistent customer decisions, and compliance issues.

A practical next step for retailers planning 2026

Levi’s plans to roll out its super agent in early 2026 and expand through the year. That timeline is a gift to everyone else: it signals where the bar is heading.

If you’re running retail operations or e-commerce in Ireland, I’d treat 2026 planning like this:

1. Pick one painful omnichannel workflow and map it end-to-end.
2. Identify the “actions” that could be automated with approvals.
3. Build a small agentic pilot where staff already work.
4. Measure operational reliability, then scale.

This post is part of our AI in Retail and E-Commerce series, and the pattern keeps repeating: the retailers winning with AI aren’t chasing novelty. They’re building systems that make the customer journey less fragile.

If a “super agent” showed up inside your current tools next month, which workflow would you hand it first—returns, click-and-collect, or customer service triage?