The Agent as Customer's Advocate

In my last article I set out some different routes that brands may take in response to the rise of agentic search. Here I’m going to stand back a little and consider what these changes may mean for brands and eCommerce specifically. While nascent it’s pretty clear that ‘Agent mode’ is here to stay, and from here user expectations for how it should feel are going to grow. Less Minority Report, more Her. Today this agent layer is vanilla, objective, cold. As we get more expert at interacting with them - and they get better at listening to us - this will change.
In this world the agent becomes not just a passive gateway, but an active advocate and filter for the customer. Consider this future interaction:

“Place a weekly shop at Tesco. Read me my favourites for me to choose which I want to add. Then let me know what’s on offer that I might consider adding”

Here the agent has native access to my account at Tesco.com (ideally not via an agentic browser). It then retrieves first a list of favourites, then polls a service for suitable offers, curating them for me. This future of eCommerce may seem complex and perhaps far-fetched, but it will be with us soon. The question is how brands should equip themselves, moving beyond traditional marketing to a world where they are equipped to communicate with (and even negotiate through) the agent, at scale. Let’s think through what this might entail for brands.

The New Inbound: Engineering for Experience

I’ve previously suggested there may be a dividing line between experiential and functional eCommerce. On both sides of the divide however the brand will have a requirement to ensure their core content is agent optimised or at least agent readable. Content here could include product specifications, descriptions, reviews. Given advances in computer vision, images and video too. We could define this as the inbound content strategy. Its purpose is to ensure the agent can effectively find and present the brand’s best qualities when the user is actively searching for a product that might match. Part of this is undoubtedly the place of GenAI Engine Optimization (GEO) - a term that’s mildly horrific to anyone like me old enough to remember the days of doorway pages, the rise of social optimization etc. But it’s also an engineering challenge, to build the pipelines and systems that allow for seamless and efficient communication between the brand's data and the AI agent:

1. API Development & Maintenance: Engineers will be responsible for creating and maintaining robust APIs that provide agents with direct access to a brand's product catalog, customer data, and content. These APIs must be fast, reliable, and well-documented.
2. Structured Data Implementation: While marketing defines the content, engineering implements the schemas (e.g., Schema.org) and data structures that an agent can parse. This ensures that the rich metadata created by the marketing team is correctly formatted and delivered.
3. Dynamic Negotiation Systems: the team will need to build systems that can receive requests from an agent and, based on predefined rules or machine learning models, offer dynamic pricing, bundles, or add-ons in real time.
4. Data Feeds & Integrations: Engineers will manage the systems that deliver updated product information, inventory levels, and pricing to agents in a timely and efficient manner, often through continuous data feeds.

By adopting these engineering practices, a brand becomes the most compelling option not just because of its products, but because it is the most discoverable and intelligently articulated choice for a user's agent.

The New Outbound: Communicating with the Agent

I’ve referenced an idea for a ‘dynamic negotiation system’ above as an engineering challenge. It’s also of course a commercial one. We’ll need to decide how brands should ‘market’ to the agent. In this new frontier of outbound I see two key modes:

Mode 1: Notifications

In notification mode a brand could send a notification to a customer’s agent about a relevant offer or new product. Here the agent may be in ‘sentry mode’, and given a brief to minimise noise may simply decide not to let irrelevant communications through to the user. So brands will need to work much harder to personalise this kind of promotion to the right customer: personalisation will need to get, well, personal.

Mode 2: Negotiations

Here the brand’s system could interface with the agent to dynamically offer a better price, or potentially even to intervene in real time to win a competitive transaction. This moves from a static offer to a dynamic, programmatic negotiation. The implication is a far more complex set of pricing/discounting rules, able to evaluate net revenue considering a variety of variables - including, probably more importantly, the customer value to the brand.

The New Metrics of Trust and Negotiation

In this world of fluid inbound and outbound communications between users, their agents and brands we’ll need to start considering new types of business metrics that reflect the success of the relationship:

• For instance we may want to monitor the offer acceptance rate: the % of agent-relayed offers that result in a purchase.
• Or the profitable negotiation success rate: % transactions negotiated between a brand’s system and an agent at sustainable margin
• Agents themselves may start to build metrics that allow them to make better choices for their users: a ‘trust score’ based on a brand’s ability to serve relevant inventory or provide fair pricing for example These next generation metrics will by their nature be more complex to operate than today's eCommerce metrics. Unlike click-through rates or conversion percentages, they'll require sophisticated data pipelines that can track multi-step negotiations and evaluate the quality of agent relationships over time. Early adopters will need to experiment with proxy measures whilst the industry develops standardised benchmarks - much as we did in the early days of social media marketing.

4 lessons for brands to get ahead of this curve

“Predictions are difficult, especially about the future” Niels Bohr

OK so some of this is a little guess work about where we are headed. But parts of it are happening right now, in beta features and early implementations. 4 lessons for brands then:

1. Go early to get ahead: build these capabilities today and get ahead as consumer expectations evolve
2. Break down silos before they get broken down for you: view this transition not as a threat to traditional marketing channels, but as an evolution that requires new forms of partnership between marketing, engineering, and commercial teams
3. The human touch will be ever more valuable: while we may choose to hand off tasks to technology the brands that thrive will be those that can seamlessly blend human creativity with machine efficiency, creating experiences that feel both personal and scalable. The future belongs to brands that can speak fluent 'agent' whilst never losing sight of the human they're ultimately serving.
4. Product + trust + efficiency = customer value: in this new landscape, the strongest brands won't just be those with the best products, but those that become the most trusted and efficient partners for their customers' digital advocates.

The question isn't whether this future will arrive, but whether your organisation will be ready when it does.