How Agentic Commerce is Becoming the New Front Door to Retail

Agentic commerce is the shift from “customers clicking through pages” to “customers (and their AI agents) expressing intent in conversation—and having software plan, decide, and act across systems.” In Microsoft’s framing, the “front door” becomes the conversation itself, and competitive advantage is earned during the moment of choice, not only before it.

From Digital Bricks’ perspective, this is not primarily a UX change, it’s an architecture change. Winning requires (a) agent-ready product and policy data across third-party discovery surfaces, and (b) owned, brand-safe conversational experiences that create a feedback loop of intent → action → learning.

Technically, retailers should expect the “agent layer” to depend on APIs, connectors, and governed data flows: catalog, inventory, price, promos, fulfillment, loyalty, and order capabilities must be callable reliably (and securely) by agents. Platforms are also moving toward integration standards and protocols for agent interaction (for example, MCP and agent-to-agent patterns), increasing the premium on clean contracts, identity, and observability.

Digital Bricks helps retailers operationalize this shift on Microsoft’s ecosystem through: data structuring and pipelines, real-time processing, Copilot Studio agent builds, multi-agent orchestration patterns, and Microsoft-native governance using Purview/Fabric-aligned controls, paired with adoption accelerators and training so teams can sustain and scale what they launch.

How AI conversations are replacing traditional search results for retail

Retail discovery is moving from “typing keywords and scanning pages” to “asking for outcomes and constraints.” A shopper expresses intent (budget, timing, taste), and an assistant responds with a recommendation that already weighs the tradeoffs.

The behavioral signal is already visible. Bain & Company reports that 30%–45% of consumers use generative AI to research and compare products.  In practice, that means conversational interfaces are becoming a meaningful “entry point” to shopping, especially for higher-consideration purchases where fit, constraints, and confidence matter.

The implication is straightforward: retailers must be optimized for questions, not pages. That requires two parallel preparations:

First, your product truth (attributes, policies, availability, price) must be expressed in agent-readable formats—not just human-friendly merchandising copy. Microsoft explicitly calls out that many product datasets were built for filters, not conversations.  Second, the systems behind the experience must be callable—because the conversational layer is increasingly expected to do more than “recommend.” It’s expected to resolve uncertainty (“will it arrive tomorrow?”) and trigger action (“place the order,” “apply the promotion,” “start a return”).

What is agentic commerce and how is it changing the shopper experience

the “front door” is no longer a homepage or search box: it is the conversation.  McKinsey & Company similarly frames agentic commerce as shopping powered by AI agents acting on our behalf, including multistep chains of actions enabled by reasoning models.

In practical retail terms, agentic commerce is when an agent can:

Understand intent and constraints expressed in natural language (occasion, budget, fit, shipping deadlines).
Call tools/APIs to gather evidence (inventory, pricing, reviews, policies) and reconcile conflicts (“in stock online” vs “not deliverable by Friday”).
Recommend with rationale, then execute (reserve stock, apply a promo, create the cart, start checkout, initiate returns), often with a human approval step when risk is high.

From Digital Bricks’ standpoint, the best mental model is: an agent is a workflow system with a conversational UI. The differentiator is not the chat bubble—it’s what sits behind it: retrieval, planning, tool use, system-of-record writes, and governed telemetry.

How AI shopping agents generate insights that drive business growth

Each conversation creates value in the moment while also producing learning that informs recommendations, promotions, assortments, and experiences over time.

To make that loop real, you need an agent architecture that separates responsibilities and treats “learning” as a first-class output. Digital Bricks typically frames this as role-based agent collaboration (not a monolithic bot): specialized agents for retrieval, planning, execution, and evaluation—coordinated through an orchestration layer.  In Digital Bricks’ public description, multi-agent systems can use patterns like planner–executor and tool-using agents, and can be built with Microsoft platforms (Azure OpenAI, Copilot Studio, Azure Logic Apps), plus orchestration infrastructure such as Durable Functions and Service Bus.

Critically, the “learning layer” depends on data flow design: conversational events and outcomes must be captured, classified, and routed into trusted analytics domains. Digital Bricks’ approach aligns this with pipeline and lakehouse patterns—ETL foundations, structured zones (raw → cleaned → curated), and real-time feeds where immediacy matters (for example, inventory volatility).

What makes the agentic commerce shift different

Retail has survived channel shifts before—store to web, desktop to mobile, owned to marketplaces. Microsoft’s claim is that agentic commerce goes further because it changes how decisions are made: a decision layer sits between shoppers and brands, and a learning layer returns signals back to merchants.

The economic upside could be meaningful at scale. McKinsey projects that by 2030, the US B2C retail market alone could see up to $1T in orchestrated revenue from agentic commerce, with global projections of $3T–$5T.

What’s “different” technically is that agents can traverse the same digital rails humans use today (web checkout, catalog APIs, loyalty systems), but they do it faster, more consistently, and across more surfaces.  That puts pressure on three areas:

Integration standards and agent protocols are emerging. McKinsey explicitly flags integration enablers such as Anthropic’s Model Context Protocol (MCP) and other agent communication/commerce protocols as part of what organizations will need to master.
Identity and trust become “non-human” problems. A growing share of traffic and actions may be initiated by agents, so authorization, consent, and auditability must be designed for service-to-service interactions—not only human sessions.
Data freshness becomes a competitive moat. An agent can only recommend (and act) if your product and policy truth is complete and current; stale price/availability breaks the experience and erodes trust.

A practical indicator of where this is going: Microsoft’s Copilot connector model explicitly supports “synced” connectors (indexing content into Microsoft Graph) and “federated” connectors (retrieving content in real time using MCP without indexing into Graph).  This is the blueprint of an agentic world: some knowledge is cached, some must be fetched live—based on sensitivity, latency, and governance constraints.

How to build competitive advantage in agentic commerce

Be discoverable wherever shoppers ask, and build owned agentic experiences that capture learning, then do both. The battle is increasingly fought at the API and data-contract layer. Discovery can happen anywhere, but “trustworthy execution” can only happen if your systems expose governed, reliable actions.

Ensure discoverability wherever shoppers ask

Discoverability in the agentic era is not only SEO. SEO wins clicks; AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) win recommendations in AI-powered discovery—where completeness and current data drive clarity and credibility.

In operational terms, “discoverable to agents” means your product truth is:

Structured (attributes and taxonomy that map to shopper intent, not just category filters).
Current (price, availability, fulfillment promises pulled from authoritative systems and refreshed continuously).
Contextual (benefits and use cases that match how customers ask questions).

Digital Bricks enables this “agent-ready catalog” by treating product data as an engineered asset. Their public service model includes ingestion/parsing, schema design, entity identification, and conversion into machine-readable formats optimized for analytics and AI systems.  For retailers with fragmented sources (PIM, ERP, supplier PDFs), Digital Bricks also positions Intelligent Document Processing as a path to extract and normalize product and policy data into downstream systems, built on Power Automate or Azure AI services depending on scale and complexity.

On the API side, the practical goal is to expose a minimal “agent commerce surface area” reliably:

Catalog search & product detail APIs (including variants and compatibility).
Inventory availability APIs (store-level and DC-level, with reservation semantics).
Pricing and promotion APIs (including eligibility rules).
Fulfillment, returns, and policy APIs (shipping cutoffs, returns windows, warranties).

In Microsoft’s ecosystem, this is often implemented through connectors and actions: Copilot Studio can call connectors as tools at the agent level or inside topics, enabling agents to execute tasks against external services.

Build owned agentic experiences that capture learning

Third-party discovery can create awareness, but owned conversational experiences create advantage because they let brands capture the context behind decisions and feed it back into merchandising and personalization.  This is also where trust concentrates: Bain reports consumers trust retailers’ on-site agents three times more than third-party agents (at least today).

Owning the agent experience does not mean building everything from scratch. It means owning:

The brand voice and policy truth presented to shoppers.
The integration layer to systems of record (so answers and actions are correct).
The learning loop (telemetry, experimentation, and data rights).

Digital Bricks’ Agent Adoption Accelerator offering and Agent Factory development services are designed around building custom agents with knowledge, actions, and analytics: deployed across channels to assist employees and customers.  When you need more than a single agent, Digital Bricks explicitly positions multi-agent orchestration as the way to coordinate specialized agents, enable task routing, tool use, and memory/context management (including vector search and persistent stores).

Why the winning move is doing both

If you’re only present on third-party agents, you risk being commoditized, your offers are compared, but your differentiation and learning stay outside your control.
If you only build owned agents and ignore third-party discovery, you may lose share of “early funnel” influence as more shoppers start with AI-guided exploration.

The durable strategy is a two-way flywheel: strengthen discoverability through complete, current data, and strengthen owned experiences to learn faster—then feed that learning back into both your owned surfaces and your external representation.

If agentic commerce is the new front door, then “being ready” is not a slogan. It is a set of contracts your business can keep in real time. Agents will only recommend what they can understand, and they will only act where systems are callable, authorised, and observable. Retailers that treat product truth, policy truth, and execution APIs as first-class assets will earn trust at the exact moment customers decide. Retailers that do not will still show up, but as interchangeable inventory inside somebody else’s decision layer.

Digital Bricks helps retailers make that shift concrete on Microsoft’s ecosystem. We take you from data that was designed for filters to data designed for intent, from siloed systems to governed, tool-ready capabilities, and from a single assistant to a multi-agent architecture that can plan, execute, and learn safely. The outcome is practical: higher-quality recommendations, fewer failed promises, faster resolution of uncertainty, and a measurable learning loop that improves conversion, service cost, and merchandising decisions over time. In an agentic market, the winners are not the loudest brands, they are the most executable ones, and we build that execution layer with you.