> ## Documentation Index
> Fetch the complete documentation index at: https://kb.aampe.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Aampe Glossary

> Lots of funny new words when working with agentic infrastructure

**Agent**\
An AI entity assigned to each user, learning preferences through interactions and optimizing messaging in real time.

**Agentic Infrastructure**\
The system of agents, content, and labels that enables true 1:1 personalization at scale.

**Agentic Learning**\
Continuous adaptation through trial, feedback, and reinforcement, balancing exploration and exploitation.

**Alternates**\
Variant options for a single message component (e.g., three different CTAs) that agents mix and match.

**Components**\
Building blocks of a message (Greeting, Value Proposition, Offering, Incentive, CTA, Tone, Other).

**Content as Infrastructure**\
Approach where messages are not campaigns but a reusable, labeled library that fuels learning.

**Content Coverage Map**\
A structured inventory mapping product features and workflows to Topics, showing where content exists and where gaps remain.

**Contextual Bandit**\
A machine learning framework where agents select actions (e.g., messages) based on user context, balancing personalization with learning.

**Evergreen Content**\
Always-on messages (e.g., feature education, FAQs) that form the bulk of agent learning.

**Exploration vs. Exploitation**\
The balance agents strike between testing new content and using proven, high-performing variants.

**Inner Voice Statement**\
User-centered phrasing of a value proposition (e.g., “I want to feel in control of my finances”).

**Labels**\
Semantic tags (e.g., “Convenience,” “Urgency”) applied to alternates, enabling agents to reason and compare systematically.

**Message Group**\
A set of related messages made by combining alternates and labels, tested together for personalization.

**Offering**\
The specific product, feature, or benefit highlighted in a message.

**Reinforcement Learning (RL)**\
Learning approach where agents adjust strategies based on observed user behavior and outcomes.

**Reward Function**\
The feedback mechanism agents use to determine whether a message or action led to a desirable outcome.

**Surfaces**\
Channels or placements where messages appear (push, email, SMS, in-app, web).

**TACIR (Target Agent-Customer Interaction Rate)**\
The ideal frequency of interactions per week between agents and users, used as a diagnostic for content sufficiency.

**Thompson Sampling**\
A probabilistic method agents use to select actions, balancing exploration of new options with exploitation of known successes.

**Three-Fold Agentic Content Strategy**\
Framework dividing content into Evergreen (60%), Triggered (30%), and Tactical (10%) categories.

**Topic**\
A high-level communication category (e.g., “Welcome,” “Churn Rescue”) that bundles offerings, audiences, and contexts.

**Triggered Content**\
Event-based messages delivered in response to user actions (e.g., cart abandonment, subscription pause).

**Value Proposition**\
The core reason users should care about an offering, often tied to human values (e.g., Control, Trust).