Meta teases the arrival of agentic commerce: What it means for brands
Last Updated:
Feb 2, 2026
During Meta’s latest earnings call, Mark Zuckerberg made it clear that agentic commerce is moving from concept to product inside the company.
Zuckerberg described new “agentic shopping tools” designed to help people find “the right very specific set of products” from businesses in Meta’s catalogue. He also signalled that monetisation of Meta AI is now firmly on the roadmap, pointing to a mix of subscriptions, advertising, and commerce-led revenue as these systems reach greater scale and capability.
That direction was reinforced by Chad Heaton, Meta’s Vice President of Finance, who highlighted growing traction in AI-powered discovery and business messaging, alongside early experimentation with paid AI features for businesses.
Taken together, these comments suggest Meta is positioning AI agents not as a thin layer on top of its existing ads business, but as an active intermediary between consumers and merchants.
What Meta means by agentic commerce
Zuckerberg’s framing is precise. He does not talk about generic product recommendations, but about helping users find “very specific” products from Meta’s catalogue. That implies agents that can interpret intent, ask follow-up questions, narrow options, and transact on the user’s behalf.
This is a step beyond today’s discovery model, which is driven primarily by passive scrolling and ad targeting. Agentic commerce implies:
Conversational product discovery rather than feed-based browsing
Persistent memory of preferences and constraints
Decision-making that feels delegated, not assisted
Heaton’s reference to “new product experiences to make it easier for people to discover and buy new products across our apps” supports this. Discovery and conversion are being collapsed into a single AI-mediated flow.
In practice, this likely means AI agents embedded across WhatsApp, Instagram, Messenger, and Meta AI surfaces, capable of guiding users from intent to purchase without leaving the app.
Possible implementations of agentic commerce at Meta
While Meta has not announced a single flagship “agentic commerce” product, the earnings call points to several likely implementation paths. Rather than a standalone launch, this is more likely to emerge as a set of AI-powered behaviours across existing surfaces.
1. Inside Meta AI
The most direct implementation is within Meta AI itself, functioning similarly to ChatGPT or Google’s AI search experiences.
Here, users would express intent in natural language, for example asking for a specific product, budget, or use case. The agent would then query Meta’s commerce catalogue, narrow options, and present a short list of products that can be purchased or reserved directly.
Zuckerberg’s reference to helping people find “the right very specific set of products from the businesses in our catalogue” strongly supports this model.
Monetisation would likely follow two parallel paths:
Organic recommendations, where Meta earns a referral or transaction fee
Paid placements, where merchants pay for preferential inclusion in agent responses
This mirrors how search monetisation evolved, but with the ranking logic increasingly controlled by AI rather than keywords.
2. Passive agentic actions inside the feed
A second, more ambient implementation would sit inside Instagram or Facebook feeds.
Instead of actively querying an agent, users might encounter products with an embedded “buy for me” or “find this for me” action. Tapping this would delegate the purchase or product comparison to an AI agent, which could confirm size, colour, delivery, or alternatives before completing the transaction.
This aligns with Meta’s strength in passive discovery and would allow agentic commerce to blend into existing behaviour, rather than requiring users to adopt a new interface.
From a business perspective, this keeps ads central while increasing conversion by removing friction at the point of intent.
3. Within Meta’s business messaging products
This is the most explicitly evidenced path based on Chad Heaton’s comments.
He highlighted over one million weekly conversations between people and business AIs, particularly in WhatsApp-heavy markets. This suggests Meta already sees messaging as a commerce surface, not just a support channel.
In this model, agentic commerce lives inside WhatsApp, Messenger, or Instagram DMs. Users ask questions, negotiate options, and complete purchases within a conversational flow, assisted by AI but grounded in a specific business relationship.
This is particularly powerful for:
High-consideration purchases
Services rather than physical goods
Repeat or local commerce
For merchants, this shifts value from storefront design to conversational quality and data integration.
What this means for merchants
Agentic commerce fundamentally changes where competition takes place.
Rather than competing for attention in a feed or bidding for clicks, merchants will increasingly compete to be selected by an AI agent acting on the customer’s behalf. Visibility shifts from being seen by the user to being understood and trusted by the system.
That has several practical implications.
Structured data becomes a primary growth lever
If AI agents rely on Meta’s commerce catalogue to evaluate and recommend products, then product data quality directly determines visibility.
Incomplete, inconsistent, or poorly structured catalogues will be disadvantaged, regardless of brand strength or ad spend.
Merchants should prioritise:
Clean, consistently maintained catalogue feeds
Clearly defined attributes, variants, and use cases
Transparent pricing, delivery timelines, and returns policies
Agents cannot recommend products they cannot confidently explain or compare.
Product context matters as much as product features
Agentic shopping experiences are likely to be highly personalised, drawing on Meta’s extensive user data to match products to specific needs, situations, and preferences.
This increases the importance of contextual clarity. Merchants need to be explicit about:
Who the product is for
What problem it solves
When and why it is the right choice
Vague positioning that relies on brand recognition or aesthetic appeal will perform poorly in agent-mediated flows. Precision in describing use cases and constraints helps agents match products to the right intent.
Organic social content may become part of Meta’s discovery logic
Meta has a unique advantage compared to other AI platforms: access to a vast, first-party archive of social content across Instagram and Facebook.
As AI models increasingly synthesise third-party signals to inform recommendations, it is plausible that Meta’s agents will draw on organic social content to understand products, usage, sentiment, and visual context.
Given LLM’s multimodal capabilities, this would be not just written copy, but images & videos all potentially influencing how products are interpreted and surfaced by agents.
For merchants, this elevates the strategic value of high-quality, product-focused social content beyond brand awareness alone.
How merchants can prepare now
Agentic commerce is still emerging, but the direction of travel is clear. Merchants can take practical steps today to avoid being caught unprepared as AI agents begin to mediate discovery and choice.
Audit and improve Meta catalogue quality
Ensure product data is complete, accurate, well structured, and consistently updated across all Meta commerce surfaces.
Meta’s catalogue documentation already provides a useful proxy for the type of structured data Meta’s systems rely on today, including product titles, descriptions, attributes, variants, availability, pricing, and imagery. These same inputs are likely to form the foundation for agentic product discovery.
The better your catalogue data, the easier it is for AI systems to understand, compare, and recommend your products.
Clarify product positioning and use cases
Be explicit about who each product is for, what problem it solves, and when it should be chosen over alternatives.
Agentic systems will favour products with clear, well-defined use cases over vague or generic positioning. Precision helps agents match products to specific user intent rather than broad interest signals.
Invest in high-signal organic content
Prioritise organic social content that shows products in use, explains context, and demonstrates real-world outcomes.
As Meta’s AI systems draw on multimodal signals, visual and experiential content may increasingly shape how products are understood and surfaced by shopping agents, not just how they are perceived by human audiences.
Prepare for paid AI access
Monitor Meta’s emerging AI ad products closely. Advanced agent capabilities, priority placement, or enhanced discovery features are likely to become paid distribution channels over timey.
Merchants should plan for AI access as a future line item in their acquisition mix, not a free layer.
Understand your visibility inside Meta AI
As agents intermediate choice, traditional metrics like followers, clicks, and impressions will matter less. What matters more is how often and in what context your brand is recommended by AI systems relative to competitors.
Merchants should begin tracking share of voice in AI-driven environments and understanding how their products are being interpreted and positioned. Tools such as Azoma can help assess AI visibility inside Meta and other agent-driven surfaces as this shift accelerates.
A shift in who controls the buying journey
Meta’s earnings call points to a future in which the platform, not the merchant website, increasingly controls how products are discovered, evaluated, and chosen.
Agentic commerce does not remove ads or branding, but it reframes them. Merchants are no longer marketing solely to people, but to AI systems acting on people’s behalf.
For businesses that adapt early, this creates leverage and defensibility. For those that delay, it risks quiet invisibility.
The rollout may be gradual, but the strategic direction is already clear.
Article Author: Max Sinclair