Agentic Commerce: What Does It Mean, and How Can Brands Participate?

Agentic Commerce: What Does It Mean, and How Can Brands Participate?

What is Agentic Commerce?

Agentic commerce is the new form of online shopping, where autonomous AI agents browse, search, compare, negotiate, and purchase products or services on behalf of the user. As Salesforce defines it, “ Agentic commerce uses AI to act on behalf of users or businesses.” Rather than humans manually navigating websites, AI agents use natural language to understand user intent, such as “find me the best laptop deal” or “book me a flight to Japan”, and then execute the entire process end-to-end. This includes discovery, evaluation, comparison, and transaction.

AI chats such as ChatGPT, Gemini, and Claude are rapidly evolving beyond search and recommendations into full AI commerce systems. These platforms are increasingly capable of completing multi-step, autonomous, and proactive workflows, from product discovery and price comparison across multiple vendors to checkout and fulfillment, as enabled by payment and infrastructure providers like Stripe .

The result is a highly personalized, efficient, and frictionless commerce experience driven by AI agents rather than traditional interfaces.

How can brands prepare and participate in Agentic Commerce?

For brands, participating in agentic commerce requires more than experimentation:

• It requires intentional preparation.
• Brands must ensure their digital assets, product data, and brand experiences are optimized to be discovered, interpreted, and acted upon by AI agents operating inside conversational environments.
• This means shifting from optimizing solely for human browsing behavior to optimizing for AI retrieval, reasoning, and action.

The rise of conversational platforms such as ChatGPT is fundamentally reshaping how consumers discover, evaluate, and purchase products. This shift is not about advertising inside AI, it is about embedding brands directly into the conversational fabric of AI systems.

According to Forbes , 57% of consumers already use AI for product research, while reluctance to trust AI for purchases dropped from 66% to 32% in just eight months. This rapid change in consumer behavior makes AI commerce not a future consideration, but a present competitive requirement.

When we talk about selling in AI chats or Agentic Commerce, we are not referring to chatbots handling basic customer support. We are describing a broader shift toward conversational and action, taken by agents, where AI platforms themselves become direct channels for discovery, recommendation, and transaction.

• This represents a fundamental evolution beyond traditional e-commerce, moving away from static websites and keyword-driven search toward dynamic, interactive dialogues that guide customers from initial interest to completed purchase.
• Research from Sprinklr shows that 58% of online shoppers are at least moderately interested in using conversational AI tools for shopping, particularly in categories such as apparel, electronics, and home goods.

At the core of this shift is the concept of an AI-native brand experience. In this model, a brand is not merely present; it is an active participant in the conversation.

For example, when a user asks, “What’s a good pair of running shoes for marathon training with high arch support?” an AI-native brand experience can surface specific product recommendations, contextual comparisons, and direct purchase options within the conversational flow itself. This is where the real power of selling in ChatGPT emerges.

What are the benefits of AI commerce for brands?

1. Guided Selling Through AI Agents: One of the most immediate applications of agentic commerce is guided selling. AI agents can function as virtual sales assistants, helping users navigate complex buying decisions through tailored recommendations, comparisons, and real-time answers to product questions. According to Zoovu's 2026 Benchmark for AI in Ecommerce Conversion report for AI in Ecommerce Conversion, AI-powered guided selling can increase conversion rates by up to 25%.
   • This approach is already validated at scale. Amazon reported that customers who interact with Rufus, its AI shopping assistant, are 60% more likely to complete a purchase, according to its report: How Rufus scales conversational shopping experiences to millions of Amazon customers with Amazon Bedrock . Importantly, this demonstrates that brands do not need to invest millions to benefit from AI-guided selling; leveraging AI chats provides similar advantages.
2. Always-On, Real-Time Engagement: Agentic commerce also transforms customer engagement. AI assistants provide instant, 24/7 responses—handling questions, offering guidance, and supporting purchasing decisions at any time. According to BusinessDasher , 91% of customers globally expect real-time assistance, reinforcing why AI-native interfaces are becoming a baseline expectation rather than a differentiator.
3. Direct Transaction Facilitation: While native payment processing inside AI chats is still evolving, agentic commerce already enables seamless transitions from conversation to transaction. AI agents can guide users from discovery to a secure checkout flow—or, in some cases, complete transactions directly within the AI environment. Reducing friction at this stage is critical, as conversational continuity significantly accelerates purchase decisions and improves completion rates.

How Agentic Commerce works?

This new era of commerce is enabled by the core capabilities of large language models (LLMs). As highlighted by Philip Wels on Medium , LLMs excel at understanding natural language, synthesizing unstructured information, and suggesting next actions based on data patterns, context and intent. These capabilities unlock key agentic commerce functions, including:

• Hyper-personalized product discovery: AI agents can analyze user preferences, conversational context, and prior interactions to deliver highly relevant recommendations—far beyond traditional recommendation engines.
• Autonomous decision-making: Agents can compare options, evaluate trade-offs, and progress toward a transaction with minimal user input.
• Action-oriented execution: AI can move seamlessly from insight to action, completing bookings, purchases, or subscriptions within the same conversational session.

This is where specialized platforms like TEDIX play a strategic role. TEDIX enables brands to deploy AI-native experiences across major AI platforms with full brand control, structured data access, and zero-friction integration. By combining conversational design, controlled knowledge layers, MCP-compatible integrations, and official ChatGPT apps, TEDIX ensures brands can participate confidently in agentic commerce, without sacrificing accuracy, trust, or brand integrity

H ow brands technically enable Agentic Commerce

Enabling agentic commerce is not a single feature; it is an architectural shift. Brands must combine structured product data, conversational logic, transactional integrations, and brand governance into a system that AI agents can reliably access and act upon. At a technical level, agentic commerce is enabled by a layered architecture that allows AI agents to safely access brand data, reason over it, and take action on behalf of users. While implementations vary by industry, the core structure remains consistent across successful deployments.

1. Structured Data & Commerce Systems (Foundation Layer)

Everything starts with clean, structured, and machine-readable data. This includes product catalogs, pricing, availability, policies, and transactional endpoints hosted in systems such as e-commerce platforms, CRMs, booking engines, or internal databases. Without this foundation, AI agents cannot reliably retrieve or act on brand information.

2. Integration & Action Layer (APIs + MCP)

Above the data layer sits the integration layer, where APIs expose brand capabilities to AI systems. This is increasingly standardized through mechanisms like the Model Context Protocol (MCP), which defines how AI agents securely access external tools, datasets, and actions with explicit permissions and constraints. MCP enables AI agents to move beyond passive answers and perform real operations—such as fetching live inventory, comparing offers, or initiating a checkout, while maintaining strict control over what the AI is allowed to do.

3. AI Reasoning & Orchestration Layer

Large language models such as ChatGPT operate at this layer, interpreting user intent, reasoning across multiple data sources, and deciding the next best action. This is where agentic behavior emerges: the AI plans, evaluates trade-offs, and executes multi-step workflows instead of returning a single response.

4. Conversational Interface & App Layer

This layer is where the user interacts with the system. Through official ChatGPT apps and SDKs, brands can deploy controlled conversational experiences that include UI components, structured responses, and action triggers. Rather than redirecting users to external websites, discovery, evaluation, and conversion can all happen inside the AI conversation.

5. Transaction, Analytics, and Control Layer

Finally, successful agentic commerce systems include monitoring, analytics, and governance. This layer tracks user engagement, prompt triggers, conversion events, and AI behavior, while enforcing brand tone, factual accuracy, compliance, and ethical AI standards.

How can I control what AI says about my brand?

Agentic Commerce makes brand control an imperative. As brands operate inside AI platforms, maintaining strict brand control becomes essential. Without governance, AI systems risk generating off-brand responses, inaccurate product details, or inconsistent messaging, issues frequently discussed within communities such as the Reddit r/ChatGPT .

To mitigate these risks, brands must implement robust control mechanisms, including:

• Controlled knowledge bases: Brands must provide AI with a curated, verified knowledge base of product information, FAQs, and company policies. This prevents the AI from "hallucinating" or generating incorrect details, which can be detrimental to customer trust and lead to legal repercussions.
• Real-time content updates: Ensuring the update of product information, promotions, and brand messaging in real-time is crucial. This ensures that the AI always provides the most current and accurate information, reflecting dynamic business changes
• Ethical AI governance: Establishing clear ethical guidelines for AI interactions, including transparency about when a user is interacting with AI and how their data is used, is vital for building and maintaining customer trust, according to Swifterm .
• Custom branding and tone control: Providing the AI with interaction guidelines on brand-specific voice, rules, and communication standards.
• Real-time Analytics Integration: Tools to monitor AI performance, track brand mentions, and analyze user interactions to ensure alignment with brand guidelines and business objectives, according to Fashionunited.in ..

Trust is a critical success factor. PwC's 2024 AI Business Predictions highlight that responsible AI oversight is essential for sustaining customer confidence and achieving reliable business outcomes.

Without the technological structured architecture previously mentioned, brands risk being passively mentioned, or worse, misrepresented, by AI agents. With it, brands can become active, trusted, and operational participants in agentic commerce ecosystems.

Platforms like TEDIX are designed to abstract this complexity. By combining structured data ingestion, MCP-compatible integrations, conversational design, analytics, and brand control into a single system, TEDIX enables brands to deploy AI-native commerce experiences across ChatGPT and other AI platforms without rebuilding their infrastructure from scratch.

Ranking to AI Visibility: from SEO to GEO (Generative Engine Optimization):

Agentic commerce requires a fundamental shift in how brands think about visibility. Traditional SEO focused on ranking web pages for human clicks. In AI-driven environments, visibility is no longer defined by rankings, but by whether AI agents can retrieve, trust, and reuse brand information inside generated answers and autonomous workflows.

This is the new Generative Engine Optimization (GEO).

• GEO focuses on ensuring that brand content is discoverable, semantically aligned, and eligible for citation by AI systems.
• Brands must optimize for how large language models retrieve and synthesize information, rather than how search engines index and rank pages.
• If your brand isn't optimized for AI discovery, you're effectively invisible to a growing segment of your potential customer base.

This shift is already visible in user behavior. Research from McKinsey's AI Discovery Survey shows that 44% of users are comfortable relying solely on AI-generated summaries from platforms like ChatGPT or Google Overviews instead of visiting brand websites. As AI agents increasingly act as intermediaries, brands that are not retrievable within these systems risk becoming invisible to a growing share of their audience.

The business impact of GEO is material. According to Adomantra , brands with strong AI visibility report a 38% increase in direct branded searches and a 52% lift in website leads, without additional advertising spend. In agentic commerce, GEO is not about traffic acquisition; it is about becoming a usable, citable, and trusted input to AI decision-making.

This isn't just about B2C. Gartner predicts that 60% of B2B sales organizations will transition to data-driven selling by 2025, leveraging AI and predictive analytics to optimize buyer engagement and close deals faster, according to Accio . The ability to sell in ChatGPT extends to B2B contexts, where AI can assist with lead qualification, personalized outreach, and even proposal drafting, significantly increasing sales productivity by up to 30% and improving customer satisfaction by up to 25%, as noted by SuperAGI . The future of commerce is conversational, and AI is leading the charge, making it easier for prospects to make decisions by reducing friction in the buying process (as reported by Benchmarkemail ).

AEO: Optimizing for How AI Answers and Acts

While GEO ensures that a brand is retrieved, Answer Engine Optimization (AEO) determines how that brand is represented once it appears inside an AI-generated response. AEO focuses on structuring content so it can be clearly understood, accurately summarized, and confidently recommended by AI systems.

In agentic commerce, AEO is critical because AI agents do not simply surface information; they guide decisions and take actions. Well-optimized content enables AI systems to explain products clearly, compare options accurately, and suggest next steps that align with user intent. This includes answer-first content, clear product differentiation, structured comparisons, and decision-ready information.

The importance of AEO extends beyond consumer commerce. Gartner predicts that 60% of B2B sales organizations will adopt data-driven selling by 2026, leveraging AI and predictive analytics to improve engagement and close deals more efficiently. Platforms cited by SuperAGI report that AI-assisted selling can increase sales productivity by up to 30% and improve customer satisfaction by 25%.

AEO ensures that when AI systems respond, they do so with clarity, accuracy, and brand-aligned messaging, turning AI answers into measurable commercial outcomes.

Why GEO and AEO Matter for Agentic Commerce

In agentic commerce, AI agents do not browse websites; they retrieve, reason, and act. GEO determines whether your brand enters the agent’s consideration set. AEO determines whether it is recommended, explained correctly, and selected.

Together, GEO and AEO form the visibility and performance layer of Agentic Commerce. Brands that invest in both are not just discoverable, they are operational inside AI conversations, decision flows, and autonomous purchasing journeys.

Actionable strategies for deploying your brand in AI conversations

Deploying your brand effectively within AI Chats requires a structured, strategic approach. It's not enough to simply have a presence; you must optimize that presence for discovery, engagement, and conversion. Below is a list you need to know to create compelling AI-native experiences that resonate with your target audience.

1. Define Your AI Commerce Objectives: Before diving into implementation, clearly articulate what you aim to achieve with AI commerce. Are you focused on:
   • Enhancing customer service
   • Driving direct sales
   • Improving product discovery, or
   • Generating leads?

For example, a luxury fashion brand might prioritize personalized styling advice and exclusive product previews, while a consumer electronics brand might focus on technical support and guided troubleshooting. Deloitte's insights suggest that organizations moving with speed, intent, and structure will gain an edge in leveraging generative AI for retail.

1. Develop an AI-Native Content Strategy: Traditional website content isn't always suitable for conversational AI. You need content that is: concise, easily digestible, and optimized for natural language processing (NLP). This involves:
   • Structured Data and Schema Markup: Implement comprehensive schema markup across your digital properties. This provides AI crawlers with clear, detailed information about your products, improving relevance and boosting your chances of ranking on AI-driven platforms (as reported by Maktalseo ).
   • Conversational FAQs and Product Descriptions: Rewrite your FAQs and product descriptions to be more conversational and answer common questions directly. Think about how a customer would ask about a product in a chat, rather than how they would read a static page.
2. Implement Robust Brand Control Mechanisms: This is where Tedix's value proposition becomes critical. Ensure that your AI deployment includes:
   • Centralized Knowledge Management: A single source of truth for all brand and product information that your AI can access and reference. This minimizes inconsistencies and ensures accuracy.
   • Content Moderation and Filtering: Test, Iterate, and Optimize Continuously. AI commerce is an evolving field. Start with pilot programs, gather user feedback, and continuously refine your content. Monitor key metrics such as engagement rates, conversion rates within the AI interface, and customer satisfaction scores. IBM Research highlights that AI-powered customer experiences can be continuously improved by learning from every interaction.

This iterative approach ensures your AI-native experience remains relevant and effective.

Real-World Use Cases:

• AI-Powered Storefronts: Imagine a user asking ChatGPT, "Find me a place in Rome to stay for 5 days with a budget of 400 euros ." An AI-powered storefront could present curated options, complete with details of the place, customer reviews, and direct purchase links, all within the chat interface. Like the current ChatGPT app of Booking.com
• Interactive Product Guides: An experience travel brand specialized in providing travelers with hiking trails can add value to the conversations by offering in AI Chats, a visualization of the maps with the hiking trails, with the AI assistant acting as a representative, suggesting which trail best suits the goals of the travaler "mauntany, short, long, etc" suggesting complementary rouths and even providing path previews lik AllTrails currently do with is ChatGPT app.
• Personalized Customer Support: Beyond basic FAQs, an AI assistant could help a customer troubleshoot a complex technical issue with a product, guiding them step-by-step or even initiating a return process seamlessly, like for example, Lovable , with its ChatGPT App, ChatGPT can see the same thing as the user and suggest potential solutions.
• Lead Qualification for B2B: A SaaS company can engage potential clients by providing valuable information and answering initial questions about their topic of expertise, and qualify leads before handing them off to a human sales representative (as reported by Nusparkmediagroup ).

By embracing these actionable strategies, brands can move beyond simply reacting to the rise of AI and proactively shape their future in the dynamic world of AI commerce.

Measuring Success: AI Search Analytics and ROI

One of the core challenges brands face in the still-emerging AI commerce landscape is measurement. Traditional e-commerce analytics, built around page views, sessions, and click-through rates, are insufficient for capturing how value is created inside conversational and agentic AI environments. Yet, understanding return on investment (ROI) is essential to scaling and optimizing any AI-driven commerce strategy.

As Gartner highlights, one of AI’s primary advantages in sales is its ability to analyze data continuously and improve performance through automation. To realize this benefit, brands must adopt a new generation of analytics designed specifically for AI-native interactions

Some of the Key Metrics for AI Commerce Performance brands must focus are:

• Brand Mentions and Sentiment Analysis: Brands must monitor how often, and in what context, their products/services and messaging appear in AI-generated conversations. Tracking brand mentions and sentiment within AI answers helps assess visibility, perception, and alignment with brand positioning. Sentiment analysis techniques documented by Nutshell illustrate how conversational data can surface opportunities to refine messaging and improve AI-driven interactions.
• AI Referral and Crawler Tracking: Understanding where demand originates is critical. By implementing AI-specific tracking tags and log analysis, brands can identify which AI platforms are generating traffic or leads, how AI crawlers interact with their sites, and which conversational entry points lead to conversion. This provides visibility into AI-assisted discovery paths that traditional attribution models overlook.
• ChatGPT App Metrics and In-Chat Performance: Deploying a native ChatGPT app gives brands not only control over how their information is presented but also direct access to engagement and conversion metrics within the AI environment itself. These include:
  • Engagement Metrics: Number of conversations, average conversation length, task completion rates, and user feedback signals (e.g., thumbs up/down) and the completion rate of tasks (e.g., product found, purchase initiated).
  • Conversion Rates within AI: Directly measure how many AI-guided interactions lead to a desired outcome, such as adding a product to a cart, completing a purchase, or signing ups.

According to findings referenced by AgentiveAIQ reports , AI shopping assistants can increase add-on sales by up to 15% and recover as much as 17% of abandoned carts, demonstrating the measurable revenue impact of optimized conversational flows.

Collecting data is only the first step. The real value lies in connecting AI interaction metrics to business outcomes, revenue, lead quality, customer lifetime value, and operational efficiency. This is where AI search analytics becomes a strategic advantage rather than a reporting exercise.

**** TEDIX addresses this measurement gap by providing real-time AI search and conversational analytics across multiple AI platforms. By deploying and tracking a Native ChatGPT App, GEO- and AEO-specific KPIs, such as AI visibility, prompt-level engagement, in-chat conversions, and attribution, brands gain a clear, actionable view of how their AI-native experiences perform throughout the customer journey.

This data-driven approach enables continuous optimization, ensuring AI investments are not only experimental, but accountable—delivering sustained improvements in engagement, conversion, and revenue as agentic commerce scales.

Overcoming challenges and future outlook in AI Commerce

While the opportunities in AI commerce are vast, brands must also be prepared to navigate several challenges to ensure successful and sustainable deployment. Addressing these proactively will be crucial for long-term success. SwiftERM AI Technology outlines key challenges such as data quality, data privacy, and integration with existing systems.

Common Challenges:

• Data Privacy and Security: AI systems rely on extensive customer data, raising concerns about privacy and security. Brands must ensure compliance with regulations like GDPR and CCPA and maintain transparency about data handling to build customer trust – a finding from Bkplussoft .
• Algorithmic Bias and Hallucinations: AI algorithms can inherit biases from training data, leading to discriminatory or unfair outcomes. Additionally, generative AI can sometimes produce inaccurate or fabricated information, known as "hallucinations." Robust testing, diverse training data, and continuous monitoring are essential to mitigate these risks.
• Integration Complexity: Integrating AI solutions with existing CRM, ERP, and e-commerce platforms can be complex and costly. Seamless integration is vital for a unified customer experience and efficient operations, per Swifterm research.
• Maintaining the Human Touch: While AI offers efficiency, customers still value human empathy and interaction. The challenge lies in striking the right balance between automation and human intervention, ensuring AI enhances, rather than replaces, meaningful human connections, according to IBM .
• Talent Shortage: There is a significant shortage of skilled AI and machine learning talent, making it challenging for brands to build and manage sophisticated AI commerce solutions in-house (as reported by Swifterm ).

Future Outlook:

Despite these challenges, the future of AI commerce is incredibly promising. We can anticipate several key trends:

• Hyper-Personalization: AI will enable even more granular personalization, anticipating customer needs and offering tailored experiences before they are explicitly requested.
• Multimodal AI: AI assistants will increasingly integrate voice, vision, and text, allowing for richer, more intuitive interactions. Imagine a user showing an AI a picture of an outfit and asking for similar items to purchase.
• Deeper Integration with IoT: AI commerce will extend into smart devices and IoT ecosystems, enabling seamless purchases through connected homes and vehicles.
• Agentic AI: The rise of agentic AI, capable of autonomously handling complex tasks and orchestrating workflows, will further transform sales and customer service, reducing seller burden and enhancing customer experiences, which Gartner has documented.

Brands that proactively address the challenges and strategically embrace these future trends, leveraging platforms that offer brand control and real-time analytics, will be well-positioned to lead in the evolving AI-native commerce landscape. Deloitte's AI Institute emphasizes that organizations that move with speed, intent, and structure will gain the edge in this transformative era.

Companies like TEDIX specialize in enabling this level of brand control. By transforming brands into AI-native experiences, they ensure zero-configuration deployment to major AI platforms while providing complete brand control with custom branding. This means brands can confidently deploy their presence in ChatGPT and other AI environments, knowing that every interaction is on-message and on-brand. Without this meticulous control, the benefits of AI commerce—such as personalization and efficiency—can be overshadowed by the risks of brand dilution and customer dissatisfaction. PwC's 2024 AI Business Predictions emphasize that trust in AI will be critical, requiring responsible AI practices and oversight to achieve reliable results.