Discover how Aampe's agents employ causal analysis to accurately measure user engagement outcomes, even when influenced by external messages. By isolating the effects of their own actions from other variables, Aampe ensures precise attribution and effective decision-making in a complex messaging environment.

Explore how agentic systems define and execute decisions. This article delves into the five key decision types—Go/No-Go, Context, Creative Policy, Item Recommendation, and Freshness—that guide autonomous agents in delivering personalized user experiences. Learn how these systems prioritize meaningful choices to enhance engagement and effectiveness.

This post shares insights about event ingestion service's architecture, which handles millions of events daily across various data providers while ensuring data isolation and processing guarantees. In this section, we’ll focus on the Publisher-Subscriber (Pub/Sub) patterns that power our service. We ingest events and translate them into a common format for the Aampe agents to learn from. These events are published and then consumed by downstream processes. We use Google Cloud Pub/Sub for our architecture.

Explore Aampe's innovative approach to personalization, combining classical recommender systems for item selection with real-time reinforcement learning agents for dynamic message composition. Learn how this dual-path strategy enhances user engagement and content relevance.