Agentic AI and AI‑ready data: Transforming consumer‑facing applications

Consumer behavior is on the brink of a dramatic shift thanks to Agentic AI – autonomous intelligent agents that act on behalf of users. Unlike traditional chatbots or static assistants, these AI agents can proactively navigate apps and websites, make decisions, and execute tasks end-to-end. From ordering groceries to booking travel, consumers will increasingly rely on AI agents to handle interactions that once required direct human effort​. For businesses, this represents a massive opportunity and a strategic challenge: capturing the transformational potential of agentic AI requires AI-ready data, especially rich behavioral data, to power these agents’ intelligence. In this article, we explore how agentic AI is poised to revolutionize consumer-facing applications and why AI-ready behavioral data is the key to building intelligent, customer-centric agents.

The rise of agentic AI in consumer applications

A new generation of AI agents is rapidly emerging in the consumer technology landscape. These agentic systems differ from basic bots by autonomously taking action to achieve user-defined goals. One high-profile example is OpenAI’s Operator – an AI agent that can browse the web and perform tasks via its own browser. Announced as a research preview in early 2025, Operator can fill out forms, click buttons, scroll pages and complete online transactions with minimal user input​. In other words, it transforms AI “from a passive tool to an active participant in the digital ecosystem,” as OpenAI put it, streamlining tasks for users and enabling new kinds of customer engagements that drive higher conversion rates​.

Major tech players and startups alike are racing into this agentic era. Here are a few developments signaling the rapid evolution of agentic AI systems:

• OpenAI’s Operator and Consumer Agents: OpenAI’s Operator launched with pilot partners like DoorDash, Uber, Priceline and Instacart, validating the potential of generative AI taking agency on behalf of shoppers​. Users can share preferences and credentials with Operator, which then autonomously “peruse available options and buy on behalf of shoppers”​. This heralds what industry experts call the Agentic Commerce Era, a third wave of digital commerce innovation on par with the rise of e-commerce and mobile shopping​.
• Agent-to-Agent Communication (A2A): To enable AI agents to work together across platforms, Google introduced the Agent2Agent (A2A) Protocol at Cloud Next 2024. A2A is a new open standard for “dynamic, multimodal communication between different agents without sharing memory, resources, and tools”​. In short, it’s a lingua franca that lets an agent from one company (say, a retailer’s customer service AI) communicate and cooperate with an agent from another (say, a logistics provider’s AI) seamlessly. Early endorsement from 35+ software vendors underscores the momentum behind A2A​.
• Model Context Protocol (MCP): Another key piece of the ecosystem is the Model Context Protocol, an open standard emerging as a “USB-C port for AI applications”. MCP standardizes how applications provide context and tool access to large language models. Originally proposed by Anthropic engineers and quickly embraced by OpenAI, MCP provides a unified interface for AI agents to discover and invoke external tools and data. With broad open-source adoption, “it’s clear that MCP has won” as a de facto standard for connecting AI agents with the world.
  
• Agentic Frameworks (LangChain and beyond): The open-source community has catalyzed agentic AI’s rise through frameworks that make it easier to build and deploy agents. Projects like LangChain have exploded in popularity, enabling developers to chain LLM reasoning with tool usage and create custom agent behaviors. In fact, we are seeing a “renaissance in personal productivity hacking, powered by agentic frameworks and process automators from LangChain and CrewAI to n8n and Make”​. According to a late-2024 survey of 1,300 AI practitioners, 51% of organizations were already using AI agents in production, and 78% planned to implement them soon – a testament to how quickly agentic systems are gaining traction.

Agentic AI is no longer theoretical; it’s here and rapidly maturing. From empowering consumers with personal shopping bots to connecting enterprise software via standard protocols, these intelligent agents are set to become fixtures in everyday digital life. The question now is how businesses can harness this technology to transform their customer experience – and that hinges on data.

Agentic AI’s transformational potential for customer experience

The promise of agentic AI is a fundamentally more customer-centric digital experience. Today’s websites and apps are typically designed around the company’s own goals and generic user flows, which often forces customers into a one-size-fits-all journey. As Snowplow co-founder Yali Sassoon observes, “Websites today are designed around the goals of the enterprises that own them. The next generation of agentic websites will reflect the needs (and contexts) of the users visiting them.” In other words, agentic applications have the potential to flip the web’s script, prioritizing each visitor’s unique intent instead of the brand’s predetermined funnel.

Consider a common scenario: a customer visits an online retail site with a specific goal in mind – say, to return a product. In the current paradigm, that customer likely has to hunt through menus or FAQs to find the return policy and follow a series of steps manually. The site is primarily built to sell, not to handle post-purchase tasks gracefully, and it shows. (It’s no surprise that perhaps ~98% of an e-commerce site is geared toward selling, yet only ~2% of visitors actually convert to a purchase.) Now imagine the agentic AI future: the customer simply tells an AI agent what they need. If an AI agent understands a visitor’s intention, it can create a bespoke experience just for that visitor. So if the visitor asks the agent “how do I return this product?”, the agent can provide all the necessary information​, personalized to that user.

Agentic AI applications can dynamically tailor a website’s content and flow to each user’s intent, delivering a bespoke experience. For example, an agent that knows a customer’s order details and preferences can reconfigure a retail site on the fly to help that customer return an item or discover relevant products, rather than forcing them through a one-size-fits-all interface​.

Crucially, the agent could even pull in context about that customer – checking their order history and location to give step-by-step return instructions specific to the item they bought – making the process as simple as possible​. If another customer arrives with a different goal (say, browsing a category), the agent can just as easily shift to a curated, magazine-like shopping experience tailored to that individual’s tastes​. In effect, every user gets a unique journey optimized for their needs in the moment. This is a radical departure from today’s static UX. Rather than customers contorting themselves to navigate sites, the AI agent does the heavy lifting – orchestrating the interface, content, and calls-to-action to suit the customer.

Key capabilities of agentic AI applications that enable these personalized experiences include:

• Understanding intent: Agents can triangulate a user’s true goal from multiple signals – from what the user explicitly asks, to their real-time behavior on the site. If uncertain, the agent can even ask clarifying questions and learn more about what the customer wants​.
• Leveraging context and data: Agents don’t operate in a vacuum; they can fetch customer-specific data and background information as tools. For example, an agent might pull up a user’s past purchases, loyalty status, preferences, or other context to inform its recommendations. This deep customer knowledge allows for highly personalized interactions.
• Dynamic content generation: Powered by large language models (LLMs), agentic systems can generate tailored content – from text to images or even UI code – on the fly​. The agent can thus compose a custom page or message for the user (“Here’s how to return your blue jacket bought last week, step-by-step…”) rather than relying on a generic webpage.
• Real-time adaptation: Agentic AI is not a set-it-and-forget-it flow; it continuously observes how the user responds at each step and adapts accordingly​. If the user seems confused or unsatisfied, the agent can adjust its approach in real time – much like a skilled salesperson would alter their pitch based on a customer’s reactions.

Together, these capabilities allow agentic applications to deliver unique, intent-driven experiences for each customer. The outcome is a web or app experience that feels more like an interactive concierge and less like a static brochure. As Sassoon puts it, “AI-powered agents will help us accomplish specific tasks more efficiently… We’ll be able to tell these applications what we want, and they will give us exactly what we want. No longer will we have to wrangle a website designed to meet one set of needs to try and get it to meet our needs”​.

From the business perspective, this shift holds tremendous promise. Happier customers who get what they need faster are more likely to convert, return, and remain loyal. Brands will be able to engage customers far more effectively when those customers are on their digital properties with intent (e.g. ready to accomplish something)​. In essence, agentic AI could usher in a new era of digital customer experience that is truly user-first and friction-free.

AI-ready behavioral data: The fuel for intelligent agents

Achieving this agentic vision isn’t just about advanced AI models – it requires data. Specifically, it requires the right kind of data: AI-ready behavioral data. AI agents need to perceive the state of the world (and the user) in order to reason and act effectively. That means feeding them streams of high-quality information about user actions, events, and context in real time.

So, what exactly is “AI-ready” data? Simply put, “AI-ready data is structured, high-quality information that can be easily used to train machine learning models and run AI applications with minimal engineering effort”​. It’s data that meets key criteria like being well-structured, clean, and enriched with context, so that an AI system (or a data scientist) can interpret it and draw insights without heavy preprocessing. AI-ready data comes with comprehensive metadata (schema, definitions) to be understandable by humans and AI alike, it maintains a consistent format across historical and real-time streams, and it includes governance/lineage to ensure accuracy and trust​. In short, it’s analytics-grade data prepared for AI.

When it comes to powering AI agents for consumer-facing applications, the most critical data is customer data – the granular events capturing what users (and their devices or apps) are doing. Clicks, page views, form submissions, product adds to cart, errors, dwell times, purchases – these behavioral events form the digital trail that agents can learn from and react to. An agent is only as perceptive as the data it’s given. If your AI agent is “flying blind” with sparse or poor-quality data about user behavior, it cannot deliver the rich experiences described above.

This is why many forward-thinking companies have invested in event-driven architectures and real-time behavioral data collection. In an event-driven setup, every user action or system change emits an event into a stream, forming a live feed of what’s happening in and around your application. These event streams essentially serve as the eyes and ears of an AI agent. “Ambient agents listen to an event stream and act on it accordingly”, as defined by LangChain’s introduction of ambient agents. Snowplow’s Alex Dean further explains that intelligent applications (like AI agents) “need to perceive, reason, and react – they need what Jay Kreps has called a ‘central nervous system’”​. In practice, that “nervous system” is often a pipeline of behavioral events flowing from user touchpoints to the AI in real time.

The link between ambient agent AI and event data is direct:

• Agents need to perceive the world as it happens.
• Event streams provide a real-time narrative of what users are doing and what’s occurring in software systems.
• Agents can subscribe to these streams to understand context and trigger the right actions at the right moments.

Snowplow’s platform is purpose-built for this kind of behavioral data streaming. It enables organizations to capture every user interaction across websites, mobile apps, and other digital products in a structured, rich event format – then funnel those events in real time to data warehouses, AI models, or other destinations of choice. By taming raw event firehoses into coherent behavioral event streams, Snowplow provides the fuel for AI agents to have continuous situational awareness. As Dean notes, “our event streams describe the behavior of customers (and now agents) in real time, providing an obvious way to power perception for customer-facing ambient agents”. In other words, Snowplow’s data pipeline can serve as the eyes and ears of your AI agents, feeding them the AI-ready behavioral data they need to make intelligent decisions.

Equally important, first-party behavioral data is a source of competitive advantage for brands building AI agents. Your company’s proprietary data about your customers – their journeys, preferences, and histories with your brand – is something external “AI middlemen” won’t automatically have. A brand-owned agent armed with rich first-party data can offer a more personalized and effective experience than a generic third-party agent. “Brand-owned agents are uniquely positioned through their deep knowledge of the customer”​, as Sassoon points out. In contrast, if you leave engagement to someone else’s agent (for example, a general-purpose assistant from a big tech provider), that agent might not leverage the nuanced data that differentiates your customer experience. This is a strong argument for investing in robust data infrastructure and AI-ready data pipelines now, so that your future AI agents will be as informed and contextual as possible.

Owning the agentic customer experience (or letting others own it)

With agentic AI on the rise, brands face a strategic choice: Will your customers be served by your own AI agents, or by someone else’s? The answer could determine who controls the customer relationship in an agent-mediated world. Imagine a consumer’s personal shopping agent (perhaps an Operator-like assistant) helping them choose products – do you want that agent recommending your brand’s products, or a competitor’s? If you haven’t equipped your digital properties with agent-friendly capabilities or built an agent yourself, you might be at the mercy of outside agents and their priorities.

“There is a huge amount at stake,” Sassoon writes. “If you invest in customer-facing AI agents, you stand to gain more loyalty, differentiation, and market share. If you don’t, you risk AI-native middlemen inserting themselves between you and your customers, chipping away at your brand’s relevance.”​ In other words, brands that move first to create useful AI agents (or agentic applications) can strengthen their customer relationships, while laggards may see intermediaries (external agents, possibly run by platform companies or even competitors) erode their direct touchpoints.

Forward-looking brands are already reimagining entire customer journeys with agentic applications. These could take the form of brand-owned AI assistants that help customers research products, make decisions, or get support within the brand’s ecosystem. For example, a travel company might offer an AI trip planner that handles end-to-end booking for the user, rather than ceding that role to a third-party agent. By doing so, the brand maintains control over the customer’s experience and data, and can craft the journey to maximize satisfaction and business outcomes.

There are several compelling reasons for a company to build its own AI agents or agent-enabled applications:

1. Owning the end-to-end experience: A brand-specific agent allows you to control the customer experience from start to finish, ensuring consistency with your brand values and service standards. You’re not relying on an outside AI to represent (or misrepresent) your offerings.
2. Differentiated, personalized service: By leveraging your proprietary customer data and domain expertise, your agent can deliver a uniquely tailored experience that generic agents can’t match. This competitive advantage can drive preference for your brand​.
3. Deeper customer relationships: An agent that interacts directly with your customers on your behalf can strengthen engagement and loyalty. It’s an always-available touchpoint that can proactively assist customers, leading to more frequent and meaningful interactions with your brand.
4. Richer data and insights: When you deploy your own agents, you also gain access to the data on those agent-customer interactions. This expands your customer intelligence. Rather than losing visibility to third-party AI intermediaries, you can capture event data from agent interactions and feed it back into your analytics (a virtuous cycle to improve both the agent and your broader strategy).

On the flip side, if you let outside agents dominate, you may lose on all the above fronts. An external AI could become the gatekeeper of customer engagement, reducing your website or app to a mere backend that the AI pulls data from. In such a scenario, your brand risks being commoditized – much like how some retailers were disintermediated by search engines or aggregators in the past. All of the differentiated value will accrue to the provider of the agentic system; if your service is obscured behind a standardized interface, then you are replaceable over time by a cheaper or better substitute​. This is analogous to the “dumb pipe” problem telecoms faced, and indeed some commentators have warned that companies could become “dumb tools” for someone else’s AI if they don’t act​.

The stakes make it clear: brands should be excited about this future and actively prepare for it. The good news is that the playing field is still relatively open. As Sassoon noted, “anyone can create customer-facing agentic experiences… you do not necessarily need to be a travel brand, for example, to build an agent that books a holiday on someone’s behalf”. In other words, incumbents and new entrants alike have the opportunity right now to build valuable AI agents for various consumer workflows. Those who seize this moment can set the terms of engagement to their advantage.

Preparing for an agentic world: Snowplow’s role and next steps

Implementing agentic AI for customer-facing applications requires more than clever algorithms – it demands a strong data foundation and instrumentation of your digital experience for AI consumption. This is where Snowplow fits into the ecosystem. Snowplow, as a leader in Customer Data Infrastructure (CDI) for AI, provides the backbone that lets organizations own and unlock the value of their customer behavioral data​. In practical terms, Snowplow’s technology enables you to generate AI-ready data streams and build the analytical infrastructure needed to support intelligent agents.

Here are a few ways Snowplow helps businesses get agent-ready:

• Data Collection & Enrichment: Snowplow’s pipeline collects raw behavioral events from all your digital touchpoints and enriches them with context (e.g. user identifiers, product metadata, session info). The result is high-quality, well-structured event data – exactly what AI agents need to understand user behavior. This data is unified and schemaed, making it easy for LLMs or AI models to interpret.
• Real-time event streaming: Snowplow can stream events in real time to downstream systems. This means your AI agents can receive a live feed of what users are doing on your website or app moment to moment. By subscribing to these Snowplow event streams, an ambient agent gains the perception of real-time context, allowing it to react instantly to user behaviors (e.g. an agent sees a customer struggling at checkout and can step in).
• Data Warehouse & Feature Feeds: All behavioral data is also stored in your cloud data warehouse (Snowflake, Databricks, etc.) in a query-ready form. This provides a goldmine for offline AI training (learning user preferences, building recommendation models) and for agents to fetch features or facts about a user when needed. For instance, an agent could query aggregated metrics (via an API or feature store fed by Snowplow data) like a user’s lifetime spend or past support issues to personalize its interaction.
• Agent Interaction Tracking: Snowplow not only tracks human user behavior – it can track agent interactions as well. As your site or app starts seeing traffic from AI agents, Snowplow can capture those events: which pages or APIs an agent accessed, what it attempted, where it succeeded or had errors. This is critical for Agent Experience Optimization (AXO), a new discipline analogous to SEO. AXO means designing and tuning your digital properties so that AI agents can seamlessly discover content, evaluate options, and transact. With Snowplow, you gain visibility into how external agents are engaging with your site, and how your own agents are performing. This insight allows continuous optimization of the experience for both human users and AI agents. (In fact, Snowplow is actively building infrastructure specifically to help brands track and understand agent interactions on their websites and applications.)

In summary, Snowplow provides the data plumbing and observability needed to thrive in an agentic future. It gives businesses the tools to build data-rich, context-aware agents (by supplying them the right data), and to optimize those agentic experiences (by analyzing the events generated by agents and users). It’s a virtuous cycle: better data leads to smarter agents, which create new behaviors to capture and analyze, which leads to even better agents and experiences.

The era of Agentic AI is arriving fast, and it promises to transform how consumers interact with brands online. To ride this wave, data and product leaders should start laying the groundwork now: instrument your digital products, invest in AI-ready behavioral data pipelines, and envision what intelligent agents could do for your customers. The companies that successfully marry agentic AI with rich behavioral data will redefine customer experience in their industries – making interactions more convenient, personalized, and satisfying than ever before.

Ready to explore the possibilities of agentic AI for your organization? Snowplow can help you get your data foundations in place and even accelerate your journey toward building AI-driven customer experiences. Book a demo with Snowplow to see how AI-ready data can power intelligent agents and discover firsthand how you can deliver the next generation of customer experiences. Your brand’s AI future starts with the data beneath it – and Snowplow is here to ensure that data is ready to fuel innovation.

Featured image credit