Agentic Architecture to Reduce Decision Paralysis

Bridging Data Science and Business Reality

Schaun Wheeler, PhD Anthropologist and Data Scientist discusses Agentic AI architectures and how they’re currently being used across multiple industries to increase user engagement, retention, and conversions.

Schaun offers a unique perspective on the integration of technical solutions within business environments, shifting from a focus on traditional data science applications to an innovative approach known as the agentic customer data platform (CDP).

70-90% of machine learning projects reportedly fail to add business value or reach production stages. This failure is not due to the inadequacy of the models or their execution but primarily because businesses are often reluctant or unable to adapt their decision-making frameworks to leverage model outputs effectively.

Most companies, especially those not primarily focused on engineering, face significant challenges in integrating data science into their core operational processes. The reluctance stems from existing business models and the inherent resistance to change the decision-making basis from traditional approaches to data-driven strategies.

This drives the need for Agentic CDPs.

Unlike traditional customer data platforms, an agentic CDP doesn’t just store and analyze data but actively participates in decision-making. This system uses reinforcement learning to create a dynamic model that adapts to user interactions and continuously optimizes communication strategies to enhance user engagement without increasing annoyance.

In the mobile app industry, the primary challenge has shifted from user acquisition to maintaining user attention amidst numerous competing apps. Traditional engagement strategies, heavily reliant on static rules and segments, often fail because they don’t adapt to individual user behaviors and preferences. This presentation discusses how decision connectivity issues—stemming from disjointed data sources and inadequate integration—hamper effective user engagement.

The Technical Architecture of Agentic CDPs:

  • Surrogates for anticipating user behavior.
  • Embeddings that capture and utilize user behavior patterns.
  • Edges and Weights for decision metrics.
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You can share data with Aampe using a dump into a storage bucket (AWS, GCP, Azure, etc.), via SFTP. For more details refer to our Integration Guide. Cannot find your preferred method for data sharing? Let us know and we'll be happy to figure it out with your team.

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No. The internal materials position Aampe as learning across multiple horizons and using reward functions to connect short-term signals with longer-term outcomes such as retention, revenue timing, and sustained engagement.

Aampe learns from real user behavior using tagged content, reward signals, abstraction layers, social calibration, and next-best-action policy selection to update what each agent believes from real user behavior.

No. Aampe relies primarily on real-world experiments, then uses machine learning to collect, generalize, and apply findings faster. We also use AI to support or perform tasks like writing copy, organize, and draw insights from user behavior, and identify messaging impact. Our hybrid approach of using both experimentation and machine learning avoid many of the common pitfalls of a naive ML approach.

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Absolutely! Aampe runs many of the tests and reports that a typical data science team normally would. Now apps of any size can have the same capabilities as the big guys.

Absolutely not! Even with Aampe running, you're still able to add or remove messages, constrain messages to certain time windows, send ad hoc messages, or even stop sending messages entirely.

Of course! (Although most of our customers report needing to send fewer ad hoc messages after starting with Aampe.) While Aampe is running, you can still send individual messages from your current customer communications platform.

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