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The Modern Approach: Individual AI Agents

In traditional machine learning, companies build one massive model to serve millions of users. Aampe takes a fundamentally different approach: every user gets their own dedicated AI agent. This isn’t just a technical difference, it’s a paradigm shift that unlocks truly personalized experiences at scale.

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Why One Model Per User Changes Everything

Most personalization systems work by training one large model on all user data, finding patterns that work “on average” across user segments, then applying the same logic to everyone in a segment. The result? Messages optimized for imaginary “average users” that don’t actually exist. With Aampe, each user gets their own AI agent that learns exclusively from their behavior, makes personalized decisions based on their unique preferences, and continuously adapts as their interests evolve over time.

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How Individual Agents Work

When a new user enters your system, Aampe automatically creates a dedicated AI agent for them. This agent starts learning from their very first interaction, observing only their user’s actions like app engagement patterns, message interaction history, purchase behaviors, and timing preferences. The agent continuously optimizes four key dimensions for their specific user
  • Content
  • Timing
  • Frequency
  • Channel
Each agent follows a continuous cycle: observe user behavior, learn and update personal preferences, decide on optimal message and timing, deliver the personalized experience, then measure the outcome and feed it back into learning.

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Business Impact of Individual Agents

This approach delivers unprecedented personalization with higher click-through rates from relevant content, increased engagement from optimal timing, and reduced unsubscribes through frequency optimization. Operationally, this means
  • Automated personalization at scale
  • Self-optimizing system that improves over time
  • No manual segment management required

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The Future of Personalization

Individual AI agents represent the next evolution in personalization technology. Instead of trying to predict what groups of people want, we give each person their own AI assistant that truly understands their unique preferences. Ready to see how individual agents can transform your user engagement? Explore how agents use labels to quickly optimize across millions of messages, or read up on the explore/exploit strategies agents use to balance learning with performance.

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Agent FAQs

Aampe agents are not based on LLMs. They are tiny causal-inference machines. LLMs are helpful for creating content and are increasingly a part of the Aampe front-end, but the brain of the agent is not a language model. It’s a statistical model, which is fitting because an agent’s goal is to look at user data and determine which messages are effective.
Aampe agents take action with a goal of influencing user behavior. To know if a message was effective, an agent
  • makes a forecast of expected behavior (without a message)
  • sends the message
  • observes the behavior as it really happened (with a message)
Agents estimate the impact of the message by comparing the observed behavior with the forecast. The agents then update the internal weights for each label associated with message, which improves the content and timing of future messages. Read a more detailed explanation of why Aampe agents measure the impact of their interventions using events instead of user attributes.
Algorithmic bias often arises when systems generalize insights from a majority population to and applies them to minority populations. This happens because traditional machine learning models tend to optimize for statistical averages across broad populations, reinforcing dominant patterns while neglecting individual variations.

Aampe’s agentic infrastructure avoids this by treating each user as a unique context, learning directly from individual behavior rather than aggregating across groups. With Thompson sampling over networked action sets, the system experiments and adapts at the individual level, ensuring that decisions reflect personal preferences rather than majority-driven trends, thereby reducing the structural basis for discrimination and bias.
Maybe! Here are some things to consider…
  • Do the brands have distinct customer bases? 
  • Is it important to cross-sell between brands? 
WIth a shared account, agents are aware of all outbound messages. If they just sent a message for Brand A, they will likely wait before sending another message - possibly for Brand B. You share customer touchpoints across brands, which could be a good move for the right company. 

With separate accounts, a single customer that subscribes to multiple brands will receive messages from multiple agents. The agents cannot cordinate, so it’s possible (though unlikely) that they all send messages at the same time.