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Pratik Panwar
24 Nov 2025
Insights from this edition to keep you informed and ahead.
Every major platform is quietly redesigning commerce around agents — not interfaces. When discovery, comparison, and purchasing move inside autonomous systems, traditional funnels collapse.
Your next competitive advantage won’t be a faster website or a better offer, but whether your business can be understood, reasoned over, and acted on by these agents. The companies preparing for that reality now will define the market’s next decade.
This edition breaks down what actually went live in the last 90 days, who’s experimenting seriously, and what an enterprise roadmap for 2026 should look like.
By the end of this issue, an eCommerce / digital leader should be able to:
Explain “Agentic Commerce” in one sentence to their CFO.
Name at least five concrete launches from major players in this space.
Sketch a 12-month agentic roadmap for your commerce stack: from experimentation to revenue pilots.
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Every major platform, cloud vendor, and retailer is now betting on agents, but the interesting story is how they’re wiring them into real systems. From Revelation Labs’ vantage point, the momentum is building in five technical layers at once: checkout, playbooks, workflow architecture, platform capabilities, and governance. Under the hood, this looks less like “chatbots 2.0” and more like a new application layer that sits on top of your data, APIs, and policy engines.
The first shift is agentic checkout becoming real. Instead of sending users from a chat surface to a traditional checkout page, platforms are exposing payment, discount, shipping, and tax logic as composable APIs that agents can call directly. OpenAI’s Instant Checkout and Agentic Commerce Protocol and its developer guide show how purchases can complete entirely inside ChatGPT, with Etsy already live and Shopify on the way, while PayPal’s partnership announcement with OpenAI moves users from “chat” to “checkout” with almost no extra friction.
Technically, this means decoupling the checkout experience from the UI, making payment orchestration, fraud checks, and address validation available as tools the agent can sequence. In practice, we see teams moving rules from embedded frontend code into shared services, standardizing error codes, and emitting unified events (e.g., agent_checkout.initiated, agent_checkout.authorized) so finance, fraud, and analytics teams can trace what the agent actually did.
The second shift is the rise of concrete agentic playbooks instead of generic “AI strategy.” Leading enterprises are designing narrow, high-ROI flows where agents own specific parts of discovery, evaluation, and transaction. External strategy work like McKinsey’s The Agentic Commerce Opportunity and BCG’s Agentic Commerce Is Redefining Retail – How to Respond echoes what we see on the ground: the winners start with specific journeys and KPIs, not abstract AI roadmaps.
Technically, this shows up as small, well-scoped agent skills that sit on top of vectorized product catalogs, real-time inventory feeds, and pricing engines. A typical pattern Revelation Labs implements: an LLM-based planner that interprets user intent, a retrieval layer that pulls structured product, policy, and promo data, and a set of task-specific tools (e.g., basket optimization, delivery ETA simulation) that the agent can compose. The playbook is defined as policies and KPIs, not slideware.
The third shift is that agentic workflows are entering core enterprise architecture. Architects are starting to treat agents as first-class workflow participants, not a bolt-on assistant sitting at the edge. IBM’s overview of agentic workflows and related platform announcements map closely to what we’re implementing: agents triggered by events, orchestrated via domain workflows, and grounded in policy.
From a technical standpoint, that means designing flows where the agent is triggered by events from commerce, ERP, OMS, or CRM, uses a policy engine to decide what it’s allowed to do, calls tools via secure API gateways, and writes back outcomes to systems of record. Thought pieces like HBR’s “Designing a Successful Agentic AI System” help leadership think through governance and accountable ownership for these systems. We often recommend a pattern like: domain events on a bus (Kafka, Pub/Sub, etc.), an orchestration layer that hands structured tasks to the agent, and a guardrail service that enforces limits, approvals, and escalation rules before any irreversible action (refunds, cancellations, price overrides) is committed.
The fourth shift is that retail and commerce platforms are going “agent-native” rather than simply “AI-enhanced.” Discovery, support, and merchandising use cases are being rebuilt around agents that can read, reason, and act. Shopify’s “AI agents in ecommerce” session, Practical strategies for your brand, and Zendesk’s guide to agentic workflows for CX are early public blueprints for how this looks in real stacks.
Underneath, this requires well-modeled product and content schemas, feature stores for behavioral signals, and tool APIs that let agents run search, personalization, promotion, and ticket updates in a single reasoning loop. For example, a discovery agent might use semantic search over embeddings, re-rank based on margin and inventory constraints, then call a merchandising tool to generate bundles and a support tool to preemptively surface policies that reduce returns. Revelation Labs pushes clients to think in terms of: what are the tools an agent needs, what latency budgets are acceptable, and how are responses logged so data science can analyze agent behavior like any other system.
The final shift is that legal, risk, and governance are catching up with the technology. Agentic systems can no longer be opaque black boxes; they need observable, auditable behavior. Legal analyses like Morgan Lewis’s “Agentic AI: An Agent of Change in the Ecommerce Space” highlight how liability, fairness, and transparency expectations are evolving as agents become a real touchpoint between consumers and brands.
Technically, this means implementing structured logging of every tool call, decision, and policy check; stamping each action with agent identity, model version, and approval state; and storing reasoning traces in a way that can be inspected without exposing sensitive prompts or user data. Enterprises are building lightweight policy engines that describe allowed actions per role, region, and product line, and tying those policies into both the agent’s prompt constraints and the API gateway that ultimately approves or blocks an action. Revelation Labs often helps teams establish this as a shared “agent governance layer” so engineering, legal, and security are all reading from the same source of truth instead of maintaining separate, conflicting controls.
What you can do right away!
Customer-facing: Shopping assistance, guided discovery, subscriptions, replenishment.
Business-facing: Catalog enrichment, pricing experiments, fraud checks, CX triage, supply-chain triggers.
“A clear thesis prevents scattered pilots and keeps teams aligned.”
Product, pricing, inventory, policies, customer history, and content quality.
API exposure across commerce, ERP, OMS, CRM, and support systems.
Cross-system permissioning and safe-guard rails.
Useful reference: IBM on agentic workflows
An agentic shopping assistant limited to a small SKU cluster.
An agentic CX triage agent drafting resolutions for human approval.
Measure hard outcomes: conversion rate lift, AOV changes, CSAT improvement, handle-time reduction.
Read more: Shopify on AI agents in ecommerce
