If you work with AI technology, you've likely encountered the following terms:
Machine Learning
AI Agents
AI Decisioning
While “machine learning” has been a part of the mainstream tech vocabulary for decades, “AI agents” and “AI decisioning” are relatively new terms. What do these words mean and how are they all related?
This guide will walk you through each concept in turn, providing definitions and real-world examples. By the end, you'll understand what distinguishes anAI agent from a machine learning model, and where AI decisioning fits into the landscape. Whether you're evaluating vendors, building AI solutions, or simply trying to understand the technology, these distinctions matter for making informed decisions.
Let's start with the most specific concept and work our way to the broadest.
Part 1: What is an AI Agent?
An AI agent is a system that interacts with its environment to accomplish goals through exploration and adaptation. Think of it like a rat learning to navigate a maze. It doesn't just follow predetermined paths but actively explores, learns from feedback, and optimizes its behavior over time.
While the term "AI agent" has exploded in popularity thanks to recent LLM-based chatbots, the concept itself has been around for decades. The canonical textbook on reinforcement learning - a concept that’s core to many agentic systems - was published in 1992 (Sutton and Barto, of course).
For a technology product to qualify as an AI agent, it needs three core characteristics:
1. Environmental Interaction with Feedback Loops
An agent takes actions that change its environment, observes the results, and uses that information to inform future decisions. This creates a continuous cycle where each action leads to new observations that shape future behavior.
2. Goal-Directed Behavior
Agents work toward specific objectives, whether that's maximizing user engagement, solving customer problems, or navigating to a destination. They're actively trying to achieve something measurable.
3. Adaptive Learning Through Exploration
True agents don't just execute fixed strategies. They explore different approaches, learn what works, and exploit that knowledge while continuing to test new possibilities. This balance between exploration (trying new things) and exploitation (using what works) is fundamental to agent behavior.
While the amount of exploration required for a system to qualify as agentic is subjective, better descriptors for a deterministic system include “automation,” “function”, or “program.” AI Agents are problem solvers.
Notice what's not required:
Agents don't need large amounts of memory, though it often helps
They don't need large language models (LLMs)
They don't even need to be particularly sophisticated
What matters is the closed-loop interaction with an environment where actions influence future observations. The proper dose of exploratory behavior combined with online learning make AI agents extremely well suited for many applications.
Examples of AI Agents
Customer Service Chatbots (LLM-Based)
Modern conversational AI represents one of the clearest examples of AI agents. When you chat with an advanced customer service bot:
It interacts with an environment (your conversation)
It has clear goals (resolve your issue, maintain satisfaction)
It adapts responses based on your reactions
It explores different solution approaches when initial attempts don't succeed
The agent engages in a dynamic interaction, adjusting its actions based on how the conversation unfolds.
Personalized Marketing Agents
At Aampe, we've built agents for personalizing customer interactions using techniques like Thompson Sampling, difference-in-differences, and reinforcement learning. These systems:
Explore different message timings and content variations for each user
Learn from response patterns (opens, clicks, conversions)
Balance trying new approaches with using proven strategies
Continuously adapt based on individual user feedback
Each customer interaction becomes an opportunity to both achieve immediate goals and gather information for future interactions.
This approach predates the current LLM boom (Aampe was founded in 2020, chatGPT was released in 2022), showing that an AI agent does not require the use of a large language model.
Part 2: What is Machine Learning?
Machine learning (ML), at its core, is a collection of algorithms for pattern recognition and prediction. ML models learn statistical patterns from historical data and apply those patterns to make predictions or classifications on new data.
The key characteristic of machine learning is that it transforms data inputs into useful outputs through learned patterns. A credit scoring model learns from past loan applications and outcomes, then predicts whether new applicants will repay. An image classifier learns visual patterns from labeled photos, then identifies objects in new images.
Machine learning is often a crucial component within AI agents, providing the pattern recognition capabilities they need to make sense of their environment. But ML alone doesn't make something an agent. An LLM, by itself, is not an agent. The distinction lies in whether the system interacts with and influences its environment versus simply processing inputs.
Most standard ML applications are open-loop systems. They make predictions or classifications, but those outputs don't influence what data they see next (even if those predictions are fed back into future training data). They do not seek out new data inputs when existing data inputs are unsatisfactory. It’s just pattern matching applied to whatever inputs arrive.
Examples of Machine Learning (Non-Agentic)
Spam Filters
Email spam filters have used machine learning since the late 1990s:
They classify each email independently based on learned patterns
Marking an email as spam doesn't change what the filter observes next
There's no exploration, just pattern matching against known spam characteristics
No sequential decision-making or goal-seeking behavior over time
While spam filters improve through user feedback over time, the filter itself isn't actively exploring or experimenting with different strategies.
Credit Scoring Models
Many banks currently use ML for credit decisions:
Generate risk scores based on historical patterns
Make predictions independently for each application
Don't learn from the outcomes of their specific predictions
Don't explore alternative scoring strategies
These models are powerful and useful, but they're fundamentally passive pattern matchers rather than active agents.
Part 3: What is AI Decisioning?
"AI Decisioning" describes automated decision-making systems that incorporate machine learning predictions. The term is intentionally broad and can encompass various architectures, from simple rule-based systems with ML inputs to sophisticated adaptive platforms.
This concept builds on decades of work in operations research and decision science. What's new is the "AI" branding and the integration of modern ML models. Companies have been using data-driven, automated decisioning systems for decades; they just didn't call them "AI decisioning" until recently.
At its most basic, AI decisioning works like:
Step 1: ML Models Generate Predictions
Customer lifetime value: $2,400
Fraud risk score: 0.85
Churn probability: 0.65
Step 2: Business Logic Determines Actions
Business rules (pre-programmed instructions) determine what to do with these predictions:
If fraud_score > 0.8, decline transaction
If churn_risk > 0.6, offer retention discount
If lifetime_value > $2,000, assign premium support
Step 3: System Executes Decisions
The prescribed action is automatically carried out.
The learning framework in AI decisioning can vary dramatically. Some systems are purely reactive (ML + rules), while others incorporate agent-like capabilities such as contextual bandits or reinforcement learning. The term itself doesn't specify which approach is used, making it important to understand the underlying architecture.
Examples of AI Decisioning
Traditional Loan Approval Systems
A typical implementation:
ML model predicts default probability
Business rules set approval thresholds
System automatically approves or declines applications
Each decision is independent
No learning from approval outcomes
No exploration of alternative strategies
This is AI decisioning without agent behavior.
Dynamic Pricing Platforms
Airlines have been using these systems since the 1980s (before anyone called them "AI"):
Use statistical models to predict demand at different price points
Apply business rules for margin requirements
Adjust prices automatically
Some versions test different prices to learn price elasticity (agent-like)
Others just apply predetermined formulas (non-agentic)
The same "AI decisioning" label applies to both approaches.
Part 1: What is an AI Agent?
An AI agent is a system that interacts with its environment to accomplish goals through exploration and adaptation. Think of it like a rat learning to navigate a maze. It doesn't just follow predetermined paths but actively explores, learns from feedback, and optimizes its behavior over time.
While the term "AI agent" has exploded in popularity thanks to recent LLM-based chatbots, the concept itself has been around for decades. The canonical textbook on reinforcement learning - a concept that’s core to many agentic systems - was published in 1992 (Sutton and Barto, of course).
For a technology product to qualify as an AI agent, it needs three core characteristics:
1. Environmental Interaction with Feedback Loops
An agent takes actions that change its environment, observes the results, and uses that information to inform future decisions. This creates a continuous cycle where each action leads to new observations that shape future behavior.
2. Goal-Directed Behavior
Agents work toward specific objectives, whether that's maximizing user engagement, solving customer problems, or navigating to a destination. They're actively trying to achieve something measurable.
3. Adaptive Learning Through Exploration
True agents don't just execute fixed strategies. They explore different approaches, learn what works, and exploit that knowledge while continuing to test new possibilities. This balance between exploration (trying new things) and exploitation (using what works) is fundamental to agent behavior.
While the amount of exploration required for a system to qualify as agentic is subjective, better descriptors for a deterministic system include “automation,” “function”, or “program.” AI Agents are problem solvers.
Notice what's not required:
Agents don't need large amounts of memory, though it often helps
They don't need large language models (LLMs)
They don't even need to be particularly sophisticated
What matters is the closed-loop interaction with an environment where actions influence future observations. The proper dose of exploratory behavior combined with online learning make AI agents extremely well suited for many applications.
Examples of AI Agents
Customer Service Chatbots (LLM-Based)
Modern conversational AI represents one of the clearest examples of AI agents. When you chat with an advanced customer service bot:
It interacts with an environment (your conversation)
It has clear goals (resolve your issue, maintain satisfaction)
It adapts responses based on your reactions
It explores different solution approaches when initial attempts don't succeed
The agent engages in a dynamic interaction, adjusting its actions based on how the conversation unfolds.
Personalized Marketing Agents
At Aampe, we've built agents for personalizing customer interactions using techniques like Thompson Sampling, difference-in-differences, and reinforcement learning. These systems:
Explore different message timings and content variations for each user
Learn from response patterns (opens, clicks, conversions)
Balance trying new approaches with using proven strategies
Continuously adapt based on individual user feedback
Each customer interaction becomes an opportunity to both achieve immediate goals and gather information for future interactions.
This approach predates the current LLM boom (Aampe was founded in 2020, chatGPT was released in 2022), showing that an AI agent does not require the use of a large language model.