8 CPaaS Providers Positioned to Power the AI Agent Era

AI agents are about to rewire commerce. Google’s Universal Commerce Protocol launched in January 2026. Visa and Mastercard expect agent-led purchases by Q1 2026. Amazon’s Alexa+ is booking appointments through Expedia and Square. But here’s what most marketers are missing: every one of these agent interactions runs on CPaaS infrastructure.

Messaging. Voice. Video. Verification. The platforms that power your 2FA codes and appointment reminders are becoming the nervous system of agentic commerce. And with the CPaaS market projected to reach $80 billion by 2030, the providers who get AI positioning right will capture the next decade of growth.

What’s Covered

1. Twilio: The Data-Driven Agent Infrastructure Play (Best Tip)

Twilio has repositioned from “dumb pipes” to “the brain of the agentic economy.” The key differentiator is Segment. By integrating its customer data platform directly into communications APIs, Twilio enables AI agents to understand context and identity in real-time, not just deliver messages.

At SIGNAL 2025, Twilio launched ConversationRelay for building natural voice AI agents with LLM integration, interruption handling, and real-time streaming. Voice AI grew over 20% in the latter half of 2025. Goldman Sachs upgraded the stock with price targets reaching $185.

2. GMS: White-Label AI Infrastructure for Telcos

GMS (Global Message Services) operates 900+ mobile network operator connections and has explicitly positioned as an “AI-driven communications solutions partner.” The Swiss-headquartered company recently announced a diversified business strategy to shift from legacy messaging provider to AI communications platform.

The strategic angle for agentic commerce: GMS provides white-label CPaaS solutions that enable mobile operators to offer AI agent infrastructure under their own brands. As telcos look to monetize the agent era, GMS gives them turnkey infrastructure without building from scratch. The company holds Tier 1 SMS Firewall Vendor status in the ROCCO Survey, critical for securing high-volume agent communications.

Why this matters for agent deployment:

• AI-powered SMS and Voice Firewalls provide fraud protection for automated sending
• Single API unifies SMS, WhatsApp, Viber, and RCS for multi-channel agents
• ISO/IEC 27001 and ISO 9001 certified for enterprise compliance
• Strong CIS and emerging market coverage where agent commerce is growing fastest

3. Sinch: Global Carrier Network for Agent Scale

Sinch operates over 600 direct mobile operator connections globally. For AI agents that need to reach customers across 150,000+ business deployments including Google, Uber, PayPal, and Visa, that carrier-grade infrastructure becomes critical. The Swedish company handles billions of transactions annually.

The strategic angle: Sinch’s “super network” enables smart routing for cost and delivery optimization. AI agents making high-volume bookings or confirmations across international markets need reliable delivery without managing carrier relationships in each region.

How to evaluate for agent deployment:

• Check regional coverage for your target markets
• Verify real-time delivery reporting for agent feedback loops
• Assess SMS fraud protection for automated sending

4. Vonage: Enterprise Integration for Complex Workflows

Vonage (now part of Ericsson) differentiates through enterprise tool integration. The Meetings API enables interactive voice and video workflows. For AI agents handling complex customer service scenarios that require human escalation, Vonage’s contact center integration provides smooth handoffs.

The Vonage Communications Platform supports voice, video, SMS, MMS, WhatsApp, and Facebook Messenger. AI-powered conversational interfaces are built into the platform. For enterprises already invested in Vonage infrastructure, adding agent capabilities means leveraging existing integrations rather than rebuilding.

5. Bird: Omnichannel Messaging for Conversational Commerce

Bird (formerly MessageBird) consolidated over 800 APIs into one platform. The shared inbox pulls SMS, voice, email, and messaging apps into a single view. For AI agents handling conversational commerce, Bird provides the unified interface for managing customer interactions across channels.

Bird’s strength is in Asia where they were first to connect Kakao, Line, and WeChat into a single API. As agentic commerce expands globally, that regional messaging infrastructure becomes a competitive advantage. The platform serves 25,000+ customers including banks, healthcare providers, and service apps.

Key capabilities for agent builders:

• Journey flow management for multi-step agent interactions
• Automated chatbot builders for rule-based escalation
• Number validation for identity verification

6. Infobip: Compliance-First for Regulated Agent Deployment

Infobip operates its own telecommunications infrastructure and data centers, enabling regional compliance for markets with strict data sovereignty requirements. According to Gartner’s 2025 Magic Quadrant for CPaaS, compliance differentiation is becoming decisive in enterprise procurement.

For AI agents in healthcare (HIPAA), finance (PSD2), or operating across GDPR markets, Infobip’s compliance-first architecture reduces deployment risk. The January 2025 partnership with NTT Com Online to launch NTT CPaaS in Japan demonstrates their regional expansion strategy.

7. Bandwidth: Carrier Infrastructure for Voice Agents

Bandwidth owns its own telecommunications infrastructure, operating as both a CPaaS provider and carrier. This matters for AI voice agents because network-level control provides lower latency and higher reliability than providers who resell carrier capacity.

For voice-first AI agent deployments like appointment booking systems or customer service bots, Bandwidth’s direct network ownership eliminates a layer of intermediation. The tradeoff is narrower functionality compared to full-stack platforms like Twilio, but the voice infrastructure is enterprise-grade.

8. Plivo: Developer-First for Custom Agent Builds

Plivo targets cost-conscious builders with transparent pricing and no-code agent builders. The platform recently launched AI agents with SOC 2 and HIPAA compliance, supporting LLMs from OpenAI, Google, and Meta. For teams building custom AI agent workflows without Twilio-level budgets, Plivo offers an accessible entry point.

Omnichannel delivery spans SMS, MMS, WhatsApp, voice, and webchat from a single agent interface. Built-in integrations with Shopify, Stripe, and CRM platforms reduce development overhead. The no-code workflow builder means marketing teams can customize triggers and logic without engineering dependencies.

The AI-SEO Positioning Gap

Here’s the strategic opportunity most CPaaS providers are missing: when AI search systems like ChatGPT or Perplexity need to recommend infrastructure for agent development, they’re pulling from training data and web content that positions these platforms as legacy messaging tools.

Ask Claude about CPaaS infrastructure for agentic commerce. Ask Gemini which platforms support Google’s Universal Commerce Protocol. The answers often miss the strategic pivot these companies are making. Twilio’s “CustomerAI” narrative is getting through. Most others are still positioned as SMS and voice APIs.

What’s Next

The Universal Commerce Protocol implementation launched publicly in January 2026. Shopify’s agentic storefront integration is live. Visa and Mastercard are running pilots for agent-led payments. The infrastructure layer that connects AI agents to customers is no longer theoretical.

For marketing leaders evaluating technology investments, CPaaS is no longer just about sending appointment reminders. It’s about building the communications infrastructure that AI agents will use to book, purchase, and confirm on behalf of your customers.

Start with the question: When an AI agent needs to reach your customer, what infrastructure will it use?

7 Roles Marketers Must Master to Stay Relevant in the AI Agent Era

Gartner reports that 65% of CMOs say AI will dramatically change their role in the next two years. Most are not prepared for what that change actually looks like.

The shift is not about learning new tools. It is about fundamentally repositioning yourself from executor to operator. I call this the Marketing Operator Model: a framework for the seven roles marketers must master as AI agents take over more execution work.

What’s Covered

1. Strategic Director: Set the Goals AI Cannot Define (Best Tip)

AI agents can execute toward objectives. They cannot determine which objectives matter. Your first role is defining the strategic direction that shapes everything else.

Why it matters: BCG describes the modern CMO as a “chief growth architect” who designs strategy rather than executing campaigns. AI accelerates execution. It cannot replace the judgment that decides what to execute.

How to do it:

1. Define success metrics before engaging AI. What does “good” look like?
2. Establish ethical boundaries. What should AI never do, even if capable?
3. Prioritize ruthlessly. AI can do many things. Your job is deciding which few matter.
4. Connect marketing objectives to business outcomes. AI optimizes locally. You optimize globally.

2. System Architect: Design the Workflows AI Executes

AI agents work within systems you design. Poor system design produces poor outputs regardless of how capable the AI is. Your second role is architecting the workflows, handoffs, and processes that make AI effective.

Why it matters: According to MarketingProfs research, the most advanced marketing teams have moved beyond experimentation into orchestration, embedding AI into workflows with governance, training, and measurable outcomes.

How to do it:

1. Map your current marketing workflows end to end
2. Identify which steps are routine (automate) versus novel (keep human)
3. Design handoff points where AI outputs feed into human review
4. Build feedback loops that improve AI performance over time
5. Document everything so the system scales beyond you

3. Context Provider: Supply the Knowledge AI Lacks

AI agents work with the information you give them. They do not know your company history, political dynamics, customer relationships, or industry nuances. Your third role is being the context bridge between what AI knows and what it needs to know.

Why it matters: Research on symbiotic AI identifies “Context Providers” as one of four critical human roles in human-AI systems. They supply the real-world understanding and implicit knowledge that is difficult to formalize.

How to do it:

1. Write detailed briefs that include background, constraints, and stakeholder preferences
2. Provide examples of successful past work the AI can reference
3. Include information about what not to do, not just what to do
4. Update context regularly as situations change

4. Exception Handler: Solve Problems AI Cannot Navigate

AI agents excel at pattern matching within known parameters. They struggle with novel situations, edge cases, and problems that require judgment outside their training. Your fourth role is handling the exceptions that break AI workflows.

Why it matters: Stanford’s research on human-AI collaboration emphasizes that autonomous AI agents underperform when circumstances change mid-workflow. They take liberties with decisions, encourage hallucination, or arrive at dead ends. Human exception handlers intervene at these decision points.

How to do it:

1. Define clear triggers for when AI should escalate to human review
2. Create decision trees for common exception types
3. Build in pause points for high-stakes or ambiguous situations
4. Document exceptions and their resolutions to train future AI improvements

5. Quality Controller: Validate AI Outputs Before They Ship

AI agents produce outputs at scale. They cannot judge whether those outputs meet your standards, align with brand voice, or serve business objectives. Your fifth role is the quality gate that separates usable work from AI artifacts.

Why it matters: IBM’s 2025 CMO study asks a critical question: “How prepared is your team to parse quality from quantity in AI-generated outputs?” The teams that thrive are those that build quality control into the workflow, not as an afterthought.

How to do it:

1. Establish clear acceptance criteria before AI begins work
2. Create checklists for common quality dimensions: accuracy, tone, completeness, brand alignment
3. Spot-check a meaningful sample rather than reviewing everything
4. Track error patterns to improve prompts and workflows

6. Orchestrator: Coordinate Multiple Agents and Tools

Marketing technology stacks now include dozens of AI-enabled tools. Your sixth role is orchestrating these tools into coherent workflows rather than operating each one in isolation.

Why it matters: Microsoft’s 2025 Work Trend Index found that 46% of leaders are using AI agents to fully automate workflows. But not every function evolves at the same pace. The orchestrator decides which tasks chain together, which run in parallel, and which require human intervention.

How to do it:

1. Map your AI tool ecosystem and identify overlaps and gaps
2. Design handoffs between tools. What is the output format? What triggers the next step?
3. Establish an AI council spanning marketing, IT, legal, and operations
4. Standardize prompts and workflows across the team

7. Interpreter: Translate AI Outputs for Stakeholders

AI agents produce raw outputs. Stakeholders need insights, recommendations, and narratives they can act on. Your seventh role is translating between machine output and human decision-making.

Why it matters: Wharton research on hybrid intelligence emphasizes what they call “Double Literacy”: understanding both human cognition and AI mechanisms. This is becoming a core hiring requirement for 2026 marketing roles. Interpreters bridge this gap, translating AI capabilities and limitations for executives who need to make decisions.

How to do it:

1. Synthesize AI outputs into executive summaries with clear recommendations
2. Explain confidence levels and limitations. What does the AI not know?
3. Connect AI findings to business context that stakeholders care about
4. Advocate for AI-informed decisions while acknowledging uncertainty

Final Thoughts

The Marketing Operator Model is not about defending your job against AI. It is about evolving into the role AI cannot fill. AI agents automate tasks. You orchestrate outcomes.

McKinsey’s research shows that 75% of knowledge workers already use AI tools in some form. The marketers who thrive in 2026 will not be those who avoid AI. They will be those who master the seven roles that make AI effective: Strategic Director, System Architect, Context Provider, Exception Handler, Quality Controller, Orchestrator, and Interpreter.

Which role will you develop first?

A Day in the Life: Marketing Manager 2024 vs. Marketing Operator 2026

The 2024 marketer spends most of their day on execution. The 2026 operator spends most of their day on direction, validation, and translation. Same outcomes, different leverage.

Key Concepts

How to Use Claude Cowork for Marketing Operations in 5 Steps (No Code Required)

Marketing teams spend 40 to 60 minutes per day on file management, report formatting, and data wrangling, according to OpenAI’s 2025 State of Enterprise AI report. That is time lost to tasks that add zero strategic value.

Claude Cowork changes this. Anthropic’s new AI agent can read, organize, and create files autonomously. No terminal. No code. Just clear instructions and a folder of marketing assets. I tested it on real marketing workflows the day it launched. Here is how to set it up.

What’s Covered

What You’ll Need

• Claude Max subscription ($100 to $200/month). Cowork is currently in research preview for Max subscribers only.
• Claude Desktop app for macOS. Windows support is planned but not yet available.
• Claude in Chrome extension (optional). Required for web-based tasks like accessing analytics dashboards.
• A folder of marketing files to organize or process.
• Clear task descriptions written in advance. Vague prompts produce vague results.
• Time estimate: 30 to 45 minutes for initial setup.

Step 1. Set Up Your Marketing Workspace

Create a dedicated folder for Claude Cowork and grant access through the Claude Desktop app. This sandbox approach protects your sensitive files while giving Claude the context it needs to work effectively.

Why it matters: Claude Cowork runs in a virtualized environment using Apple’s Virtualization Framework, which means it cannot access files outside the folders you explicitly share. This is a security feature, not a limitation.

How to do it:

1. Create a new folder on your desktop called Marketing-Cowork
2. Copy the files you want Claude to work with into this folder
3. Open the Claude Desktop app and navigate to the Cowork tab
4. Click “Add Folder” and select your Marketing-Cowork directory
5. Verify Claude confirms access to the folder before proceeding

Step 2. Define Your First Marketing Task

Write clear, specific instructions that tell Claude exactly what to do, what format you want, and what constraints to follow. The quality of your prompt directly determines the quality of the output.

Why it matters: Claude Cowork operates autonomously once started. According to VentureBeat’s coverage of Anthropic’s launch, it feels less like a back-and-forth conversation and more like leaving messages for a coworker. If your instructions are ambiguous, Claude will make assumptions.

How to do it:

1. State the objective: “Organize all files in this folder by campaign name”
2. Specify the output format: “Create subfolders named YYYY-MM-CampaignName”
3. Add constraints: “Do not delete any files. Move only, do not copy.”
4. Include quality checks: “Create a summary.txt listing all files and their new locations”

Example Marketing Prompts

Step 3. Connect External Data Sources

Add Connectors to pull data from Google Drive, Slack, or other integrated tools. For web-based platforms, pair Cowork with the Claude in Chrome extension to access dashboards and analytics.

Why it matters: Most marketing work spans multiple platforms. According to McKinsey’s 2025 State of AI report, the most common AI use cases in marketing involve content support, customer analysis, and pulling data across systems. Claude Cowork becomes more useful when it can access your actual data sources.

How to do it:

1. In Claude settings, navigate to Connectors and enable the integrations you need
2. Authenticate each connector with your account credentials
3. Install the Claude in Chrome extension for web-based access
4. Test by asking Claude to fetch a specific file or data point

Step 4. Queue and Monitor Tasks

Submit multiple tasks and let Claude work through them autonomously. You can queue up work and provide feedback without waiting for each task to complete. This is what makes Cowork feel like a coworker rather than a chatbot.

Why it matters: Microsoft’s 2025 Work Trend Index found that 46% of leaders are already using AI agents to fully automate workflows. The productivity gain comes from parallel execution, not faster single-task completion.

How to do it:

1. Submit your first task and let Claude begin working
2. While Claude processes, write your next task in the input field
3. Claude will queue tasks and work through them in order
4. Monitor the activity log for progress updates
5. Intervene only when Claude asks a clarifying question or flags an issue

Step 5. Review Outputs and Iterate

Check Claude’s work, provide corrections, and refine your prompts based on results. Build a library of proven task templates your entire marketing team can reuse.

Why it matters: AI agents improve with feedback. Gartner predicts that 40% of enterprise apps will feature task-specific AI agents by 2026, up from less than 5% in 2025. Success depends on iterative refinement, not one-time setup.

How to do it:

1. Review the output files Claude created
2. Check for errors, missing data, or incorrect formatting
3. Provide specific feedback: “The date format should be YYYY-MM-DD, not MM/DD/YYYY”
4. Ask Claude to redo the task with your correction
5. Save successful prompts to your prompts.md template file

Over time, your prompt library becomes institutional knowledge. New team members can use proven templates instead of starting from scratch.

Final Thoughts

Claude Cowork is not a replacement for marketing judgment. It is a tool for offloading the operational work that drains your team’s bandwidth. Start with simple file organization tasks, build confidence, then expand to more complex workflows.

The teams that win in 2026 will not be the ones with the most AI tools. They will be the ones who integrate AI agents into systematic workflows. Which task will you automate first?

Key Concepts

How to Implement the Universal Commerce Protocol in 6 Steps (Complete Technical Guide)

The Universal Commerce Protocol launched January 2026 with backing from Google, Shopify, and 20+ retail partners including Target, Walmart, and Wayfair. If you want your products purchasable directly inside Google AI Mode and Gemini, you need to implement UCP.

I’ve spent the past week digging through the UCP specification, Google’s integration guide, and Shopify’s engineering documentation. This guide walks you through the complete implementation process, from Merchant Center configuration to production deployment.

What’s Covered

What You’ll Need

Before starting, gather these requirements:

• Google Merchant Center account with products eligible for checkout
• Product data feed with accurate inventory and pricing
• Payment processor integration (Stripe, Adyen, Google Pay, or similar)
• Development environment capable of hosting REST APIs
• Return policy documentation configured in Merchant Center
• Shipping configuration with rates and delivery windows
• Customer support contact information

Time estimate: 1 to 2 weeks for basic implementation, 4 to 6 weeks for full production deployment with Google approval.

Step 1. Configure Your Merchant Center Account

Start by ensuring your Merchant Center account meets UCP requirements. Google requires specific configurations before you can enable agentic checkout on AI surfaces.

Why it matters: UCP uses your existing Merchant Center product data to surface inventory in AI Mode and Gemini. Missing configurations will block your products from checkout eligibility. According to Google’s UCP documentation, return policies and customer support information are mandatory Merchant of Record requirements.

How to do it:

1. Log into Google Merchant Center
2. Navigate to Settings > Shipping and returns
3. Configure return policies with: return cost, return window (days), and link to full policy
4. Set up shipping rates for all regions you serve
5. Go to Settings > Business information and add customer support email and phone
6. If using an advanced account, configure policies at each sub-account level

Common mistake: Configuring policies only at the parent account level when using advanced accounts. Each sub-account needs its own return policy configuration.

Step 2. Update Your Product Feed for UCP Eligibility

Add UCP-specific attributes to your product feed to signal checkout eligibility. Google recommends using a supplemental feed to avoid impacting your primary product data.

Why it matters: Products without the native_commerce attribute will not appear in agentic checkout experiences. The product ID in your feed must also match the ID expected by your Checkout API, or you need to provide a mapping via merchant_item_id.

How to do it:

1. Create a supplemental data source in Merchant Center
2. Add the native_commerce attribute set to TRUE for eligible products
3. Add consumer_notice for products requiring regulatory warnings (e.g., Prop 65)
4. Add merchant_item_id if your checkout system uses different product IDs
5. Upload the supplemental feed and verify products show as checkout-eligible

Example supplemental feed (XML):

<item>
  <g:id>SKU-12345</g:id>
  <g:native_commerce>TRUE</g:native_commerce>
  <g:consumer_notice>
    <g:consumer_notice_type>prop_65</g:consumer_notice_type>
    <g:consumer_notice_message>This product can expose you to chemicals known to the State of California to cause cancer.</g:consumer_notice_message>
  </g:consumer_notice>
  <g:merchant_item_id>checkout-sku-12345</g:merchant_item_id>
</item>

Step 3. Publish Your Business Profile

Create and host your UCP business profile at a well-known endpoint. This profile declares which capabilities you support and how agents should communicate with your systems.

Why it matters: UCP uses dynamic discovery. Agents query your profile to understand what capabilities you offer (checkout, identity linking, order management), which extensions you support (discounts, fulfillment options), and which payment handlers you accept. Without a published profile, agents cannot initiate transactions with your business.

How to do it:

1. Create a JSON file following the UCP profile schema
2. Declare your supported services (start with dev.ucp.shopping)
3. List your capabilities: dev.ucp.shopping.checkout at minimum
4. Add extensions you support: discount, fulfillment, identity_linking
5. Specify payment handlers with public keys for signature verification
6. Host the profile at https://yourdomain.com/.well-known/ucp.json

Example business profile:

{
  "ucp": {
    "version": "2026-01-11",
    "services": {
      "dev.ucp.shopping": {
        "version": "2026-01-11",
        "spec": "https://ucp.dev/specs/shopping",
        "rest": {
          "schema": "https://ucp.dev/services/shopping/openapi.json",
          "endpoint": "https://api.yourstore.com/ucp/"
        }
      }
    },
    "capabilities": [
      {
        "name": "dev.ucp.shopping.checkout",
        "version": "2026-01-11"
      },
      {
        "name": "dev.ucp.shopping.discount",
        "version": "2026-01-11",
        "extends": "dev.ucp.shopping.checkout"
      },
      {
        "name": "dev.ucp.shopping.fulfillment",
        "version": "2026-01-11",
        "extends": "dev.ucp.shopping.checkout"
      }
    ]
  },
  "payment": {
    "handlers": [
      {
        "id": "google_pay",
        "name": "com.google.pay"
      },
      {
        "id": "stripe",
        "name": "com.stripe.payments"
      }
    ]
  }
}

Common mistake: Forgetting to version your capabilities. UCP uses semantic versioning, and agents may behave differently based on the version you declare.

Step 4. Build the Checkout API

Implement the core checkout capability with endpoints for creating, updating, and completing checkout sessions. UCP checkouts follow a state machine pattern with defined transitions.

Why it matters: The checkout capability is the core of UCP. Your implementation must handle the full checkout lifecycle: cart creation, buyer information collection, payment processing, and order confirmation. According to Shopify’s UCP engineering documentation, the protocol models checkout as a state machine to handle both fully automated and human-assisted transactions.

Checkout states:

• incomplete: Missing required information. Agent should resolve via API.
• requires_escalation: Buyer input required. Provide continue_url for handoff.
• ready_for_complete: All information collected. Agent can finalize.
• complete_in_progress: Processing the completion request.
• completed: Order placed successfully.
• canceled: Session invalid or expired.

How to do it:

1. Implement POST /checkout to create a new checkout session
2. Implement PATCH /checkout/{id} to update session with buyer information
3. Implement POST /checkout/{id}/complete to finalize the order
4. Return structured error messages with severity field for agent guidance
5. Include continue_url when human escalation is required

Example checkout response:

{
  "ucp": {"version": "2026-01-11"},
  "id": "chk_abc123",
  "status": "incomplete",
  "line_items": [
    {
      "id": "li_1",
      "product_id": "SKU-12345",
      "quantity": 1,
      "price": {"amount": "49.99", "currency": "USD"}
    }
  ],
  "totals": {
    "subtotal": {"amount": "49.99", "currency": "USD"},
    "tax": {"amount": "4.50", "currency": "USD"},
    "total": {"amount": "54.49", "currency": "USD"}
  },
  "messages": [
    {
      "code": "missing_shipping_address",
      "severity": "requires_platform_input",
      "message": "Shipping address is required"
    }
  ],
  "payment": {
    "handlers": ["google_pay", "stripe"]
  }
}

Step 5. Configure Payment Handlers and AP2

Set up payment handler integration using the Agent Payments Protocol (AP2). AP2 provides cryptographically-verified authorization for agentic transactions.

Why it matters: UCP separates payment instruments (what buyers use to pay) from payment handlers (how payments are processed). This architecture lets you keep your existing payment processor relationships while enabling new instruments like Google Pay. AP2 mandate chains provide proof of user consent for every transaction, critical for fraud prevention and compliance.

How to do it:

1. Register your supported payment handlers in your business profile
2. Implement the payment handler callback endpoint
3. Configure AP2 mandate verification using your payment processor’s SDK
4. Store and verify mandate chain signatures before processing payments
5. Handle payment failures gracefully with appropriate error codes

Payment handler negotiation flow:

1. Agent sends profile declaring available instruments (Google Pay, card tokens)
2. Your checkout responds with compatible handlers for the cart
3. Buyer selects payment method and authorizes via instrument provider
4. Agent receives tokenized payment data and mandate chain
5. Your system verifies mandate and processes payment through handler

Step 6. Test and Submit for Google Approval

Validate your implementation against UCP conformance tests and submit to Google for review. Your integration must be approved before going live on AI Mode and Gemini.

Why it matters: Google reviews all UCP implementations to ensure they meet security and user experience standards. Skipping conformance testing will delay approval and require rework. The UCP GitHub repository provides conformance tests you can run locally.

How to do it:

1. Clone the UCP conformance test suite from GitHub
2. Run tests against your staging environment
3. Fix any failures, paying special attention to error handling and state transitions
4. Test the full checkout flow manually: discovery, cart creation, payment, completion
5. Join the Google UCP waitlist to request review
6. Work with Google’s team to resolve any issues during review

Conformance test categories:

• Profile discovery: Verifies your well-known endpoint returns valid schema
• Checkout lifecycle: Tests state transitions and error handling
• Payment handling: Validates mandate verification and token processing
• Escalation flow: Confirms continue_url behavior for human handoff

Final Thoughts

UCP represents the infrastructure layer for agentic commerce. Early implementers will capture AI-driven traffic as Google expands AI Mode and Gemini shopping features. The protocol is designed to work with your existing checkout infrastructure, not replace it.

Start with the core checkout capability. Add extensions (discounts, fulfillment, identity linking) after your basic flow works. The modular architecture means you can expand incrementally without breaking existing functionality.

For how UCP compares to OpenAI’s Agent Commerce Protocol, see my UCP vs ACP comparison guide.

Which step will you start with today?

How to Build an AI Marketing Agent in 6 Steps

You’ve read about AI agents transforming marketing. You’ve seen the demos. But when you try to build one yourself, it feels like you need a computer science degree just to get started.

I’ve built AI marketing agents for content production, lead scoring, and campaign optimization. This guide walks you through the exact process I use, from defining scope to production deployment. By the end, you’ll have a working agent that automates a real marketing workflow.

What You’ll Need

Before starting, gather these tools and materials:

• Python 3.10 or higher installed on your machine
• CrewAI or LangChain framework (we’ll cover both)
• OpenAI API key or Claude API key for the LLM
• Code editor (VS Code recommended)
• Marketing workflow documentation for the process you want to automate
• API credentials for tools you want to connect (optional)

Time estimate: 3 to 4 hours for a basic agent, 1 to 2 weeks for production deployment.

Step 1. Define Your Agent’s Purpose and Scope

Start by documenting a specific marketing workflow your agent will automate. The most common mistake is building a “general purpose marketing assistant” that does nothing well. Narrow scope beats broad capability.

Why it matters: Agents perform best with clear boundaries. A content brief generator will outperform a “do everything” marketing bot every time. Specificity enables better prompting, testing, and iteration.

How to do it:

• Pick ONE workflow you currently do manually (content briefs, lead scoring, campaign reports)
• Document the inputs (what data does it need?)
• Document the outputs (what should it produce?)
• Map decision points (where does it need to make choices?)
• Identify tools it needs to access (CRM, analytics, CMS)

Example scope definition:

Agent: Content Brief Generator
Input: Topic keyword, target audience, content type
Output: Structured brief with outline, key points, SEO targets
Tools: Web search, competitor analysis, keyword research
Decision: Content angle based on search intent

Common mistake: Trying to automate your entire marketing stack at once. Start with one workflow. Expand after it works.

Step 2. Choose Your Framework and LLM

Select the framework that matches your workflow complexity. CrewAI excels at multi-agent collaboration where specialized agents work together. LangChain works better for single-agent workflows with tool chaining.

Why it matters: The wrong framework creates unnecessary complexity. A simple email drafting agent doesn’t need multi-agent orchestration. A content production pipeline with research, writing, and editing steps benefits from specialized agents.

How to do it:

• If your workflow has 3+ interdependent steps requiring different expertise, choose CrewAI
• If your workflow is linear with tool calls, choose LangChain
• For LLM, use GPT-4o for speed or Claude for longer context windows
• Install your chosen framework:

# For CrewAI
pip install crewai langchain-openai

# For LangChain
pip install langchain langchain-openai

Common mistake: Choosing CrewAI for simple workflows. Multi-agent overhead isn’t worth it for single-purpose agents.

Step 3. Design Your Agent Architecture

Decide between single-agent and multi-agent architecture based on your workflow map from Step 1. Single-agent systems use one agent with multiple tools. Multi-agent systems use specialized agents that collaborate.

Why it matters: Architecture determines how your agent reasons about tasks. Multi-agent systems can handle more complex workflows but require more coordination overhead. Single-agent systems are simpler but can struggle with multi-step reasoning. The DeepLearning.AI course on multi-agent systems covers these tradeoffs in depth.

How to do it:

For a single-agent architecture (LangChain):

from langchain.agents import create_openai_functions_agent
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o", temperature=0)
agent = create_openai_functions_agent(llm, tools, prompt)

For a multi-agent architecture (CrewAI):

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Marketing Researcher",
    goal="Find competitive insights and market data",
    backstory="Expert analyst who uncovers hidden opportunities"
)

writer = Agent(
    role="Content Strategist", 
    goal="Create compelling marketing content",
    backstory="Seasoned marketer who writes copy that converts"
)

crew = Crew(agents=[researcher, writer], tasks=[...])

Common mistake: Creating too many agents. Start with 2 to 3 agents maximum. Add more only when you hit clear limitations.

Step 4. Configure Tools and Integrations

Connect your agent to external systems using Model Context Protocol (MCP) servers or native API integrations. MCP provides a standardized way to connect agents to tools without building custom integrations for each one.

Why it matters: An agent without tools is just a chatbot. Tools give agents the ability to search the web, query databases, send emails, and interact with your marketing stack. MCP eliminates the need to build custom connectors for each tool.

How to do it:

• Start with 3 to 5 tools maximum (avoid tool overload)
• Use existing MCP servers from the MCP ecosystem
• For custom tools, wrap your API calls in a tool function:

from langchain.tools import tool

@tool
def search_analytics(query: str) -> str:
    """Search Google Analytics for marketing metrics."""
    # Your API call here
    return results

Common MCP servers for marketing:

• Google Drive for document access
• Slack for team notifications
• PostgreSQL/MySQL for database queries
• Web search for competitive research

Common mistake: Connecting too many tools at once. Each tool adds context tokens and decision complexity. Start minimal and expand based on actual needs.

Step 5. Connect to Commerce Protocols

If your marketing agent needs to interact with e-commerce systems, integrate with ACP or UCP. These protocols enable agents to check inventory, process orders, and manage customer interactions across commerce platforms.

Why it matters: The agentic commerce market is projected at $3 trillion to $5 trillion by 2030. Marketing agents that can execute transactions, not just recommend products, will drive significantly more revenue than those limited to content and communication.

How to do it:

For ACP (OpenAI/Stripe) integration:

# ACP uses REST endpoints for checkout flows
# If you're on Stripe, enable agentic payments in your dashboard
# Your agent can then create checkout sessions via the ACP API

checkout_request = {
    "buyer": {"email": customer_email},
    "items": [{"sku": product_sku, "quantity": 1}]
}
response = acp_client.create_checkout(checkout_request)

For UCP (Google/Shopify) integration:

# UCP provides full lifecycle commerce capabilities
# Connect via Merchant Center or use the UCP SDK

from ucp import CommerceClient

client = CommerceClient(merchant_id="your-merchant-id")
inventory = client.check_availability(sku="PROD-123")
order = client.create_order(cart_data)

When to use each:

• ACP: Fast integration if you’re on Stripe, ChatGPT traffic focus
• UCP: Full lifecycle support, Google/Gemini traffic focus
• Both: Enterprise retailers capturing traffic from all AI platforms

Common mistake: Building commerce integrations from scratch. Use the protocols. They handle payment security, fraud detection, and compliance that would take months to build yourself.

Step 6. Test and Deploy to Production

Run your agent through edge cases, add guardrails for cost control and error handling, then deploy with monitoring. Production agents need human-in-the-loop approval for high-stakes actions.

Why it matters: A demo that works is not production-ready. Agents fail in surprising ways: infinite loops, excessive API calls, hallucinated tool calls. Testing and guardrails prevent expensive mistakes. As MarTech reports, organizations combining AI automation with human oversight see 2.4x better campaign performance than full automation approaches.

How to do it:

• Create a test suite with 10 to 20 representative inputs
• Include edge cases (empty inputs, malformed data, conflicting instructions)
• Add cost guardrails (max tokens per run, max tool calls)
• Implement human-in-the-loop for actions with real-world consequences
• Set up logging and monitoring

# Example guardrails in CrewAI
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    max_rpm=10,  # Rate limit API calls
    verbose=True  # Enable logging
)

# Human approval for high-stakes actions
if action.risk_level == "high":
    approval = await get_human_approval(action)
    if not approval:
        return "Action requires human approval"

Monitoring checklist:

• Track token usage and costs per run
• Log all tool calls and responses
• Alert on error rates above threshold
• Review agent decisions weekly for quality

Common mistake: Deploying without cost limits. A runaway agent can burn through hundreds of dollars in API calls overnight. Always set maximums.

Final Thoughts

Building AI marketing agents is more accessible than ever. The frameworks handle the hard parts. Your job is defining clear scope, choosing the right architecture, and adding appropriate guardrails.

Start with one workflow. Get it working reliably. Then expand. The teams winning with AI agents aren’t building the most sophisticated systems. They’re building the most useful ones. For more on how agents are reshaping commerce, see my comparison of UCP vs ACP protocols.

Which step will you start with today?

UCP vs ACP: Which Agentic Commerce Protocol Should Retailers Implement?

Two competing protocols now define how AI agents will shop on behalf of consumers. OpenAI and Stripe’s Agentic Commerce Protocol (ACP) launched in September 2025. Google’s Universal Commerce Protocol (UCP) launched in January 2026. Both are open source under Apache 2.0, and both will shape the $3 trillion to $5 trillion global agentic commerce market projected by 2030.

I’ve analyzed both protocols across scope, architecture, payments, integrations, and ecosystem adoption. This comparison covers what matters for retailers evaluating implementation. Read to the end for specific recommendations based on your existing infrastructure.

Quick Verdict

Don’t choose. Layer them. Use ACP for speed today, and build UCP for scale tomorrow.

ACP has a three-month head start and powers ChatGPT’s 700 million weekly users. UCP covers the full commerce lifecycle and connects to Google’s search ecosystem. Walmart and Shopify are implementing both. This isn’t about picking a winner. It’s about sequencing: ACP gets you live in weeks, UCP builds the foundation for years.

What’s Covered

1. At a Glance Comparison
2. Key Differences
3. My Analysis
4. Scope and Coverage
5. Technical Architecture
6. Payment Systems
7. Ecosystem Adoption
8. Integration Effort
9. Cost Comparison
10. When to Choose Each
11. Comparison Summary
12. Final Verdict

At a Glance

Key Differences

1. Scope of coverage. ACP handles checkout transactions only. UCP handles discovery, checkout, order management, and post-purchase support. ACP solves one problem well; UCP attempts to standardize the entire journey.

2. Payment approach. ACP uses Stripe’s Shared Payment Tokens, which are single-use, time-bound, and merchant-scoped. UCP uses the Agent Payments Protocol (AP2) with cryptographic mandate chains that create audit trails from intent to payment.

3. Integration path. ACP is Stripe-native. If you already use Stripe, enabling agentic payments takes one line of code. UCP integrates through Google Merchant Center and offers more flexibility across payment processors.

4. Production maturity. ACP has three months of live transaction processing. ChatGPT Instant Checkout with Etsy has been processing real orders since September. UCP launched at NRF 2026 with pilots announced but no production volume yet.

5. Ecosystem reach. UCP connects to Google Search, which still drives the majority of product discovery. ACP connects to ChatGPT, which processes 50 million shopping queries daily. Different platforms, different customer segments.

My Analysis

I’ve been tracking agentic commerce since OpenAI announced Operator in late 2024. The protocol war was inevitable once ChatGPT started processing shopping queries at scale. What surprised me was how fast Google responded with broader scope.

The strategic positioning is clear. OpenAI and Stripe built ACP to capture transaction revenue as AI becomes a shopping interface. Google built UCP to protect its search and advertising business by owning the infrastructure layer. Neither company is being altruistic with “open source.” Both are racing to become the default before the market fragments.

For marketing leaders watching this space, the protocol debate matters less than what it signals: AI agents are becoming a commerce channel with real transaction volume. I’ve written before about how to build AI marketing agents that connect to these systems. The retailers who treat protocols as infrastructure investment will capture share. Those who wait for a “winner” will build integrations under competitive pressure later.

Scope and Coverage

Winner: UCP

UCP covers the entire commerce lifecycle from product discovery through post-purchase support. It organizes commerce into Services (vertical domains), Capabilities (functional areas like Checkout), and Extensions (optional features). At launch, UCP supports Checkout, Identity Linking, and Order Management capabilities with roadmap items for catalog management and loyalty programs.

ACP defines four core endpoints: Create Checkout, Update Checkout, Complete Checkout, and Cancel Checkout. That’s the complete specification. It’s deliberately narrow, solving the transaction moment without prescribing how discovery or post-purchase should work.

Our take: ACP’s narrow scope means faster implementation but more custom work for everything outside checkout. UCP’s broader scope requires more upfront investment but provides fuller coverage. For teams building long-term infrastructure, UCP’s architecture is more forward-looking.

Technical Architecture

Winner: Tie

ACP’s architecture reflects Stripe’s payment processing DNA. It’s a RESTful interface with four endpoints, webhook notifications for order updates, and Shared Payment Tokens for secure credential handling. You can also implement it as an MCP server for tighter agent integration.

UCP’s architecture reflects Google’s platform thinking. Services contain Capabilities which support Extensions. This modularity means you implement only what you need. The protocol supports REST, MCP, and Google’s A2A for agent-to-agent communication.

Our take: ACP is simpler to understand and implement. Four endpoints, familiar REST patterns, and Stripe’s existing SDKs. UCP is more complete but requires understanding multiple abstraction layers. Choose based on your team’s capacity for complexity.

Payment Systems

Winner: ACP

Both protocols solve the same security problem: AI agents need to initiate payments without exposing user credentials. ACP uses Shared Payment Tokens that are single-use, time-bound, and scoped to specific merchants and amounts. Stripe Radar provides fraud detection signals. The system is production-tested with millions of transactions.

UCP uses AP2 Mandates with cryptographic proofs. An Intent Mandate defines agent permissions. A Cart Mandate captures buyer approval. A Payment Mandate authorizes the transaction. This chain creates non-repudiable evidence of consent at each step, but it’s more complex to implement.

Our take: ACP’s payment model is battle-tested and Stripe-native. If you’re already on Stripe, it’s the obvious choice. UCP’s mandate chain is theoretically more robust but has no production track record yet. For most retailers, ACP wins on proven reliability.

Ecosystem Adoption

Winner: UCP

UCP launched with the broadest coalition in e-commerce history. Co-developers include Shopify, Walmart, Target, Etsy, and Wayfair. Payment endorsers include Mastercard, Visa, American Express, Stripe, PayPal, and Adyen. Retail endorsers include Best Buy, Home Depot, Macy’s, and Sephora.

ACP has a three-month head start in production. Etsy’s entire US seller base (over 5 million sellers) has been automatically enrolled since September. Over one million Shopify merchants are eligible. Stripe’s Agentic Commerce Suite launched in December with URBN, Ashley Furniture, Coach, and Kate Spade.

Our take: UCP has broader industry backing. ACP has more production traction. The fact that Stripe, Shopify, and Etsy support both protocols tells you the industry expects coexistence. Neither will “win” in the near term.

Integration Effort

Winner: ACP

For Stripe merchants, ACP integration is remarkably simple. Enable agentic payments with one line of code. Shopify merchants using Shopify Payments are automatically eligible with no integration required. Etsy sellers were enrolled automatically when ACP launched.

UCP offers two paths: Native (use Google’s checkout interface with your Merchant Center feeds) or Embedded (maintain your custom checkout while enabling agentic transactions). Both require more configuration than ACP’s Stripe-native approach, though Shopify is building admin-level enablement.

Our take: If you’re already on Stripe, ACP takes days to implement. UCP takes weeks to months depending on your integration approach. For teams prioritizing speed to market, ACP wins decisively.

Cost Comparison

Value analysis: Both protocols are free to use. The real cost is integration effort. ACP’s Stripe-native approach means minimal engineering time for existing Stripe merchants. UCP’s broader scope means more upfront work but potentially less custom development for full-lifecycle features like order management and returns.

Verified January 2026. Check official documentation for current requirements.

When to Choose Each

When to Choose UCP

• Google Search and Gemini are primary discovery channels
• You need full lifecycle support including order management
• You want flexibility across multiple payment processors
• You’re building long-term agentic commerce infrastructure
• You need custom checkout experiences with brand control

When to Choose ACP

• You’re already using Stripe for payment processing
• You’re on Shopify or Etsy and want automatic enablement
• ChatGPT is a significant traffic source for your audience
• You prioritize speed to market over comprehensive features
• Your checkout is already optimized and you don’t want to rebuild

Comparison Summary

Reading the scores: ACP wins on speed metrics (initial integration, developer experience, production maturity). UCP wins on depth metrics (scope, architecture, ecosystem). This is why most enterprises are layering both: ACP for immediate ChatGPT coverage, UCP for long-term infrastructure.

Final Verdict

Layer both protocols. This isn’t about picking winners. It’s about building infrastructure that captures customers wherever they shop.

ACP wins on speed to market and production maturity. Three months of live transactions, minimal integration effort for Stripe merchants, and automatic enablement for Shopify and Etsy. If you need to move fast, start here.

UCP wins on scope and ecosystem coalition. Full lifecycle coverage, broad industry support, and connection to Google’s search traffic. If you’re building infrastructure for the next decade, UCP’s architecture is more comprehensive. For a deeper technical dive, see my complete UCP implementation guide.

Choose UCP first if: Google is your primary discovery channel, you need full lifecycle support beyond checkout, or you want flexibility across payment processors. The broader coalition suggests UCP will be the long-term standard.

Choose ACP first if: You’re already on Stripe, ChatGPT is relevant to your audience, or you need something live in weeks rather than months. The production track record provides confidence.

Best of both: Implement ACP now for immediate ChatGPT coverage, then add UCP as it matures. This is exactly what Walmart and Shopify are doing. Follow the enterprise playbook.

Universal Commerce Protocol (UCP): 7 Things Marketers Need to Know About Google’s New Agentic Commerce Standard

The shopping experience is about to fundamentally change. On January 11, 2026, Google CEO Sundar Pichai announced the Universal Commerce Protocol (UCP) at the National Retail Federation conference. This open standard enables AI agents to browse, compare, negotiate, and complete purchases on behalf of consumers without requiring custom integrations for every retailer.

Here’s what makes this significant: McKinsey projects agentic commerce will reach $3 trillion to $5 trillion globally by 2030. The protocol race is on, with Google, OpenAI, Stripe, and others competing to define how AI-powered shopping will work. If you’re in marketing or e-commerce, this is infrastructure you need to understand.

What’s Covered

1. What UCP actually is and why it matters (Best Insight)
2. The $3 to $5 trillion agentic commerce opportunity
3. Who built UCP and who has endorsed it
4. How UCP works technically
5. UCP vs. OpenAI’s Agentic Commerce Protocol
6. Security, payments, and merchant control
7. What this means for retailers and marketers

1. What UCP Actually Is and Why It Matters (Best Insight)

The Universal Commerce Protocol is an open-source standard that creates a common language for AI agents, retailers, and payment providers to transact together. It covers the entire shopping journey: product discovery, checkout, identity linking, and post-purchase support like order tracking and returns.

Before UCP, building agentic commerce required custom integrations between each AI platform and each retailer. That approach doesn’t scale. As Shopify’s engineering team explains, commerce is too complex and variable for monolithic point-to-point connections. UCP applies the TCP/IP pattern to commerce: layered responsibilities, clear APIs, and composable extensions.

The protocol organizes around three architectural layers:

• Shopping Service: Core transaction primitives including checkout session, line items, totals, messages, and status
• Capabilities: Major functional areas like Checkout, Orders, and Catalog, each independently versioned
• Extensions: Domain-specific schemas added through composition, such as fulfillment options, discounts, and loyalty programs

The practical benefit: implement UCP once and your products become discoverable and purchasable across Google AI Mode, Gemini, and potentially any other AI agent that adopts the standard.

2. The $3 to $5 Trillion Agentic Commerce Opportunity

The market projections for agentic commerce are substantial, and multiple research firms are converging on similar estimates. This isn’t speculative futurism. Consumers are already using AI for product research, and the transition from discovery to transaction is accelerating.

The adoption indicators are already present. According to Adobe’s holiday data, AI-driven traffic to seller sites grew 693% during the 2025 holiday season. Morgan Stanley’s survey found 23% of Americans made a purchase via AI in the past month. Half of US consumers now use AI when searching the internet.

The commercial incentive is clear. Shopping journeys that currently involve multiple tabs, price comparisons, and checkout abandonment will compress into single conversational flows. The protocols that enable this compression will capture significant platform value. Actual attribution will likely undercount early agent-mediated purchases as measurement frameworks catch up to the new channel.

3. Who Built UCP and Who Has Endorsed It

UCP wasn’t built by Google alone. The protocol was co-developed with five major commerce and retail companies and has secured endorsements from over 20 additional partners across payments, retail, and technology.

Co-Developers

Google, Shopify, Walmart, Target, Etsy, and Wayfair worked together to design the protocol. Each brought specific expertise: Google on AI infrastructure and scale, Shopify on checkout complexity across millions of merchants, Walmart and Target on enterprise retail operations, and Etsy and Wayfair on marketplace dynamics.

Shopify’s VP Vanessa Lee stated: “Shopify has a history of building checkouts for millions of unique retail businesses. We have taken everything we’ve seen over the decades to make UCP a robust commerce standard that can scale.”

Endorsements

The endorsement list reads like a directory of major commerce infrastructure:

• Payment Networks: Mastercard, Visa, American Express
• Payment Processors: Stripe, PayPal, Adyen, Worldpay
• Retailers: Best Buy, Home Depot, Macy’s, Kroger, Sephora, Ulta, Gap, Lowe’s, Chewy
• International: Zalando, Flipkart, Carrefour, Shopee, Ant International

Notably, Stripe endorsed UCP despite co-developing the competing Agentic Commerce Protocol with OpenAI. This signals that payment processors see value in supporting multiple standards rather than betting on a single winner.

4. How UCP Works Technically

UCP defines three core capabilities in its initial release: Checkout, Identity Linking, and Order Management. Each capability can operate independently or in combination, and merchants choose which to implement based on their business needs.

Checkout Capability

The checkout capability handles cart management, tax calculations, dynamic pricing, fulfillment options, and payment processing. It uses a state machine model where a checkout progresses through defined statuses: incomplete, ready_for_complete, complete_in_progress, and completed.

A key design decision: checkouts that can’t be completed entirely via API can escalate to human-in-the-loop flows. The protocol includes a requires_escalation status and continue_url field that hands off to a business UI when agent capabilities aren’t sufficient. This typically triggers during high-risk scenarios, such as age-restricted purchases (alcohol, tobacco), when shipping addresses fail AVS (Address Verification Service) checks, or when regulatory constraints require explicit human confirmation.

Identity Linking

Identity linking uses OAuth 2.0 to enable platforms to obtain authorization to perform actions on a user’s behalf. This supports loyalty programs, saved preferences, and repeat purchase flows without exposing credentials.

Order Management

The order capability handles post-purchase lifecycle events through webhooks: shipped, delivered, returned, refunded. This enables AI agents to provide order status updates and manage returns within conversational interfaces.

Transport Options

UCP is transport-agnostic. Businesses can expose capabilities via REST APIs, Model Context Protocol (MCP), or Agent2Agent (A2A) depending on their infrastructure. This flexibility means merchants don’t need to rebuild their stack for each AI platform.

5. UCP vs. OpenAI’s Agentic Commerce Protocol

UCP isn’t the only agentic commerce standard. OpenAI and Stripe launched the Agentic Commerce Protocol (ACP) in September 2025 to power Instant Checkout in ChatGPT. Both are open-source. Both preserve merchant-of-record status. But they differ in scope and ecosystem.

Competitive Surface Map

The practical reality: major retailers are implementing both. Walmart announced Google Gemini integration via UCP at NRF 2026 while already seeing 20% of referral traffic from ChatGPT. Shopify co-developed UCP while also supporting over 1 million merchants on ChatGPT Instant Checkout via ACP.

The strategic calculus is straightforward: users split across AI platforms, so merchants need presence on multiple surfaces. Protocol interoperability reduces the cost of multi-platform distribution.

6. Security, Payments, and Merchant Control

Security is the prerequisite for agentic commerce adoption. UCP addresses this through the Agent Payments Protocol (AP2), which Google developed with input from over 60 organizations including Mastercard, PayPal, Coinbase, and American Express.

AP2 uses cryptographically-signed digital contracts called Mandates to create non-repudiable audit trails from intent to cart to payment. Three mandate types handle different scenarios:

• Intent Mandate: Defines permitted agent behaviors and spending boundaries for delegated tasks
• Cart Mandate: Captures explicit user approval for a specific cart and total
• Payment Mandate: Authorizes the actual payment transaction with cryptographic proof of user consent

Payment information flows through tokenization. The user’s actual payment credentials never pass through the AI agent. Instead, scoped tokens authorize specific merchants for specific amounts, reducing fraud surface and protecting sensitive data.

The merchant control design is explicit in the protocol. Retailers remain merchant of record for all transactions. They retain ownership of customer data, set their own terms and conditions, handle fulfillment and returns, and can reject orders through their existing systems. The AI agent facilitates discovery and checkout but doesn’t intermediate the customer relationship.

7. What This Means for Retailers and Marketers

UCP shifts the competitive landscape for product discovery. If shoppers increasingly discover and purchase through AI conversations, traditional search and browse interfaces become less central. The question becomes: how do your products surface in conversational queries?

Data Quality Becomes Critical

AI agents can only recommend and transact products they can understand. UCP assumes high-quality, machine-readable product feeds with detailed attributes, accurate inventory, and complete metadata. Merchants with poor catalog hygiene will see reduced visibility and failed orders.

Google announced new Merchant Center data attributes specifically for conversational commerce: answers to common product questions, compatible accessories, substitutes, and rich media. These attributes complement traditional keywords with the semantic context AI agents need.

Channel Strategy Expands

Agentic commerce creates a new channel that sits alongside website, app, and marketplace. It requires decisions about where to enable AI checkout, what promotions to offer through agent surfaces, and how to handle the reduced touchpoints between discovery and purchase.

As Crone Consulting CEO Richard Crone noted, merchants face a tradeoff: increased discoverability versus reduced control over the last touchpoint. The product detail page and checkout experience have historically been opportunities for cross-sell and upsell. Agent-mediated checkout compresses that funnel.

Early Movers Have Advantage

Behavior shifts that took over ten years during the desktop-to-mobile transition are happening in 12 to 24 months with AI. Retailers establishing protocol presence in H1 2026 will capture traffic before the holiday rush forces mass adoption. The window for differentiation is narrow.

What Changes in 2026 vs 2024

In 2024, AI shopping assistants were demos and experiments. In 2026, they’re infrastructure with payment rails. The shift from “AI can search for products” to “AI can complete purchases” fundamentally changes the unit economics of customer acquisition. Brands that optimized for click-through are now competing for agent-selection.

What breaks if you ignore this for 12 months: your competitors establish preferred merchant status with AI platforms while you’re still debating strategy. Agent recommendation algorithms develop preferences based on early integration quality. Your product data gaps become visible as competitors with clean feeds capture the agent-mediated demand you could have served.

From a marketing systems perspective, UCP matters less as a Google product and more as the first credible attempt to standardize agent-mediated demand. The protocol itself is less important than what it represents: the formalization of AI agents as a commerce channel with its own infrastructure, measurement, and competitive dynamics.