The Problem: Decision Paralysis in Data Systems
Many businesses—especially those that aren't engineering-first—struggle to implement data science effectively. Despite significant investments in data teams and technology, they often find that insights generated by machine learning models or analytics platforms don’t lead to tangible business outcomes. This gap arises from the complexity of turning data into decisions. According to Schaun Wheeler, in his presentation “Agentic Architecture to Reduce Decision Paralysis”, businesses face bottlenecks across engineering, data science, and operations teams, which prevents them from effectively acting on data.
Wheeler's insights highlight an important problem: even when teams have access to sophisticated CRM tools and A/B testing capabilities, they often fail to use them fully. They don’t know which data to prioritize, what tests to run, or how to manage conflicting user segments. This is the root of decision paralysis—too many decisions to make, and no clear way forward.
The Solution: Agentic Architecture
Agentic architecture offers a breakthrough solution to decision paralysis by automating decision-making and reducing the reliance on manual human input. At its core, this system uses programmatic agents—software components that are assigned to each individual user. These agents learn user behaviors, preferences, and patterns over time. They then make proactive decisions about when and how to engage users based on data they collect.
Instead of human teams manually deciding who gets a message, when to send it, and what content to use, the agents autonomously figure this out. They know when a user is most likely to engage and adjust their strategy to minimize user fatigue—preventing overwhelming users with constant notifications or irrelevant messages.
For example, in a delivery app, the agentic system was able to cut SMS costs by 75% by learning which users responded to messages and which didn’t. It intelligently reduced unnecessary communication, improving overall engagement without wasting resources on users who would act without intervention.
How Agentic Architecture Works
The system learns from four core components:
Surrogates: These agents anticipate user behavior, using early indicators like adding items to a cart or viewing a product to predict the likelihood of conversion.
Embeddings: These are condensed representations of user behavior, allowing the agents to compare one user’s behavior to others. This enables the creation of control groups, which estimate the impact of doing nothing versus sending a message.
Weights: As agents learn from user responses, they assign “weights” to decisions (e.g., the best time to send a message). These weights are shared across agents, allowing them to make decisions even when there is limited data on a particular user.
Optionality: The agents can choose from a variety of messages and engagement tactics. They even have a message inventory that allows them to change the language and format of messages to keep communication fresh and effective.
This architecture creates a continuous feedback loop where agents learn, adapt, and refine their decision-making processes over time, personalizing each user’s experience.
Implications for Business Education
For students and professionals studying database design, SQL, or predictive analytics, the insights from agentic architecture provide valuable lessons on how data systems can be designed for scalability and autonomy. Let's explore how these insights apply to courses like Database Design and SQL and Predictive Analytics in an MBA in Business Analytics program.
1. Design and SQL Course
Data Integration: In the context of database design, agentic architecture highlights the importance of building databases that can handle data from various sources. In a Design and SQL course, students can explore how to design systems that integrate diverse data points—such as mobile events, website activity, and transactional data—into a unified platform. Properly designed relational databases enable the seamless flow of information that fuels agentic systems.
Optimization and Efficiency: SQL’s role in optimizing performance ties into how agentic architecture removes bottlenecks by making real-time data available. Understanding query optimization and indexing helps students recognize how critical database design is in ensuring smooth operations for systems like agentic architecture, which require fast, reliable access to user data.
2. Predictive Analytics Course
Real-Time Decision Making: In a Predictive Analytics course, agentic architecture serves as an example of how predictive models move beyond insights to actionable, real-time decisions. Students can learn how predictive models are deployed to automatically engage users, estimate behaviors, and improve user experience without human intervention. The system’s ability to test various models and adapt them on the fly is a direct application of predictive analytics in business settings.
Data Preparation and Feature Engineering: The ability of the agentic system to handle large amounts of data highlights the importance of data preparation and feature engineering. Predictive analytics students can explore how to build features that make data usable for machine learning models, focusing on how combining data from multiple sources can enhance predictive power.
Beyond Data Science: A Broader Business Impact
The beauty of agentic architecture is that it empowers businesses to leverage data without being overwhelmed by it. By reducing the human workload and allowing agents to make independent decisions, companies can optimize user engagement, reduce costs, and drive growth—all without needing huge teams to manually analyze and implement insights.
For businesses, this means that scalability and efficiency are no longer just goals but achievable realities. Agentic architecture isn’t just a technical innovation—it’s a rethinking of how decisions are made in a world overflowing with data. As companies continue to struggle with turning data into meaningful action, systems like these will become indispensable for staying competitive.
Incorporating agentic architecture into data science and business analytics coursework can help students see beyond traditional tools and understand the future of automated decision-making. By focusing on real-time, autonomous systems that optimize user engagement and eliminate decision paralysis, students can better grasp how to design and implement scalable, efficient data-driven solutions in their future careers. In a world where data is king, mastering the architecture behind it is crucial for unlocking its full potential.
Watch the video: https://youtu.be/LGpZwPmNgQQ?si=wXpVTKkdXjfRx4eo
