AI Agents & Reinforcement Learning: The Future of Customer Engagement | Jojo Zieff, Braze

 

Traditional A/B testing remains a valuable tool for marketers—it allows for quick experimentation and helps identify which version of a message or experience performs best. But its scope is limited to testing a limited number of fixed variants in isolation.

Reinforcement learning (RL) transforms static personalization to true relevance, facilitating customer experience at the individual level. Instead of relying on static tests or rules-based systems, RL continuously learns from real-time customer behavior and adapts engagement strategies across multiple dimensions. This allows brands to optimize billions of decision points across the full customer journey and delivers increasingly relevant, 1:1 experiences at scale.

More than just enhancing personalization, reinforcement learning helps marketers drive meaningful outcomes by aligning individual experiences with their most impactful business goals. It helps marketers create a deeply relevant experience for customers, while optimizing any marketer-defined goals. 

 

For many marketers, the challenge isn’t a lack of data—it’s making sense of it. Vast amounts of static data are of little value if they don’t translate into meaningful insights that can turn into action. And the complexity grows when trying to personalize and optimize experiences at scale across countless customers and touchpoints throughout the lifecycle.

This is where AI is a game changer. By leveraging behavioral and contextual data, such as a unique user's loyalty and interaction history, to help uncover insights that are not only actionable but also highly relevant to each individual. And with the rise of AI agents, we’re entering a new era where decisions about how and when to engage can be made intelligently and automatically—taking personalization efforts to the next level and delivering true business impact at scale.

 

Investment in generative AI assistants has empowered creative professionals and marketers to work more efficiently and collaboratively with AI. These tools have helped eliminate tedious tasks and bottlenecks in their process—freeing teams to focus on higher-impact work like strategy and creativity.

Now, with the rise of AI agents—systems that perceive their environment, make autonomous decisions, and take action to achieve specific goals—marketers can take creativity to the next level. These agents can run millions of simultaneous tests on creative messages, optimizing every dimension to support the most relevant experience for each individual, all at massive scale.

AI agents extend the capabilities of marketing teams by helping determine which creative components resonate most with each customer, while making sure we maintain the optimal levels of control. By putting guardrails in place, such as defining which channels or parts of the experience to optimize, and pairing agents with expert AI services that continuously fine-tune their decisions, marketers can maintain alignment with brand goals.

This balance allows marketers to stay focused on creativity and strategy, while AI dynamically experiments and personalizes content at the 1:1 level—turning great ideas into truly relevant experiences.

 

AI agents are evolving from helpful assistants to autonomous decision-makers that will fundamentally change how marketers operate. In the future, working with agents will feel less like working with a tool, and more like working with a team of specialists: a brand strategist, copywriter, developer, data analyst and more—all ready to amplify relevant customer experiences. They will also help marketers derive insights and experiment with data at an unprecedented scale, and expand personalized experiences across millions of touchpoints. 

The evolution extends beyond reactive optimization to predictive engagement, where agents anticipate customer needs before they're expressed. This shift enables AI to handle tactical execution while marketers focus on strategy, creativity, and relationship building. The objective isn't increasing message volume, but helping marketers be more strategic and relevant about when and how they reach customers. 

This shift will elevate the marketer’s role to that of a strategic conductor, guiding AI to achieve business outcomes rather than executing manual tasks.

 

When every customer interaction is truly personalized, brands unlock reciprocal value. As AI continuously learns and adapts, it can orchestrate the experiences that are deeply relevant for each customer across touchpoints. This level of precision deepens relationships, strengthens loyalty, and positions your brand as an essential part of a customer lifecycle.

With the flexibility of AI decisioning, marketers can optimize for virtually any business goal—whether it’s increasing top-line revenue, boosting customer lifetime value, or driving more loyalty sign-ups.With Braze’s recent acquisition of Offerfit, Braze’s AI agents are already supporting millions of decisions every day—and the impact doesn’t stop there. AI agents can adapt to support whatever metric matters most to your brand, helping you move faster and smarter across channels and touchpoints. 

6. How will OfferFit’s reinforcement learning capabilities reshape how you approach cross-channel customer engagement?

To resonate with consumers across both traditional and emerging channels, it’s no longer just about finding the right message, for the right channel, in the right moment—it’s finding the most relevant, end-to-end experience: the right copy and creative, combination of messages, the right sequencing of channels, and the right moments to send each message across the customer’s journey. We see the capabilities of reinforcement learning representing a fundamental shift within Customer Engagement. The successful deployment of machine-learning-driven and reinforcement-learning optimization is key to helping marketers achieve relevance at scale across the many different dimensions of a customer's experience.

Cross-channel engagement becomes truly orchestrated rather than simply coordinated—the AI determines not only message content but also optimal channel selection, financial offers, engagement timing, and more. Each interaction teaches the system something new about that specific customer, creating a feedback loop that becomes more effective over time, and delivers more relevant experiences for customers.

We are excited by OfferFit’s capabilities and how they will shape our approach for customer engagement. OfferFit AI agents make 6.4B agent decisions per day. Millions of end users are getting 1:1 personalized decisions a day - meaning marketers can orchestrate more deeply relevant experiences for their customers, at scale.