Types of AI Agents in Marketing: A Comprehensive Guide

Different types of AI agents have made big promises in marketing when it comes to efficiency and customer experience, with reports that companies using AI in marketing can achieve a 30% increase in ROI. 

A lot goes into living up to that promise, including automation, personalization, and data-driven decision-making at scale. 

However, different types of AI agents deliver differently depending on the environment and other variables at play. For example, a simple rule-based agent would automate the sending of an email in response to a specific trigger. Similarly, an advanced type of AI agent would analyze customer behavior and adjust the entire campaign in real time. 

In this article, we’ll explore different types of AI agents in detail, their working mechanisms, and how to make the right choice for your marketing workflows. 

How Different Types of AI Agents Work in Marketing Ecosystems

Marketing AI agents handle tasks across the customer journey. Based on the exclusive aspects of the journey, the roles they play vary. Some support on the personalization front, while some cater to different customer engagement tactics.

For example, CleverTap’s AI incorporates agents into the customer engagement engine to personalize outreach. The agents segment users based on their behavior and predict those who are at risk of churn. The system personalizes the messaging accordingly for optimal impact. There are predictive segmentation and journey optimization features built in that ensure each customer receives the right message when it makes the most sense for the user. 

Similarly, AI agents are deployed across several parts of the management and engagement workflows, for example: 

• Chatbots run with natural language processing capabilities to address FAQs and resolve customer issues 24/7. Customers value the instant experience here as long as the resolution is satisfactory. 
• Recommendation engines analyze user behavior and preferences to suggest relevant products or content. Take Netflix, for example; it monitors what you want and personalizes recommendations at scale. Similarly, Amazon also leverages AI to personalize shopping journeys. 

There are several other examples of AI agents assisting customers throughout their journeys, supporting them at various stages of the marketing funnel. 

Why Classifying AI Agents Matters

Classification into different types of AI agents has more business implications than academic ones. It helps businesses make better and informed choices about technology.  

You don’t want to over-engineer simple tasks with advanced AI, as it might lead to less optimal operations. Conversely, having simplistic tools to under-engineer complex scenarios is also not expected. You need to establish a delicate balance when it comes to leveraging technology. When it comes to AI agents, categorizing them into different types helps you make choices that best suit your use cases. 

The types of AI agents you choose set how smart and scalable your solution can become. You will find simple reflex agents faster to implement. However, when use cases become complex, you need agents that can work with more variables more effectively while making context-aware decisions.

Sometimes, when you need to, you can architect layered solutions with multiple AI agents working together. It keeps them intelligent and maintainable, ensuring your AI grows in capability as your marketing strategy evolves. For marketers, this becomes crucial as it helps them know what to look for under the hood when different platforms position diverse AI capabilities to stand out in the current market. 

It allows marketers to trust these platforms better and tune them accordingly.

5 Types of AI Agents In Artificial Intelligence

There are five core types of intelligent agents that form a hierarchy of increasing capabilities. 

1. Simple Reflex Agents

These are the most basic types of AI agents operating on a pure condition-action rule. It responds to the current state of the environment, with no consideration of memories or future outcomes. 

How do Simple Reflex Agents Work? (+Example)

The agent’s key mechanism is its performance element. For example, activating a traffic light. It’s solely based on the present stimulus, making it very fast and straightforward.  In marketing, a simple example of a reflex agent is a rule-based email autoresponder. For instance, when a new user signs up, a welcome email is automatically sent. 

These agents require minimal computational resources, making them highly reliable in static and predictable environments. However, the downside is that they have zero adaptability. If a situation arises that the rules didn’t anticipate, a reflex agent would fail or repeat the same mistakes. 

2. Model-Based Reflex Agents

A model-based reflex agent maintains an internal model of the world. It tracks aspects of the environment state that may not be immediately observable at every moment. These types of AI agents allow them to remember past perceptions, empowering them to make more informed decisions compared to simple reflex agents. 

How do Model-Based Agents Work? (+Example)

These agents have a knowledge state, in addition to the condition-action rules. It represents what it believes about the current environment, which is updated over time. Its actions aren’t just based on instantaneous inputs, but stem from considering “what the world is like now”. If the agents move through different states, the model updates accordingly.

These agents are suitable for dynamic environments compared to simple reflex agents. For example, consider a conversational marketing chatbot that refers to earlier messages in a conversation. A model-based reflex agent would react to a current query in light of what has been said before. 

Model-based reflex agents are more flexible but have some complexity. Their internal model needs to be designed to maintain accuracy.  If the internal model is outdated or wrong, it will directly lead to unreliable decisions. Moreover, model-based reflex agents don’t strategize beyond immediate responses. 

3. Goal-Based Agents

Goal-based AI agents act while keeping end goals in mind. It encourages them to adopt a proactive and planning-oriented approach. 

How do Goal-Based Agents Work? (+Example)

These agents are given (or they develop) explicit objectives and think of them when taking actions. The actions that help them get closer to (or reach) the goal are prioritized and executed. 

If obstacles come, a goal-based agent can re-plan or try alternative approaches. It is not bound to a fixed set of condition-action pairs; it will select whatever action appears most goal-conducive at the moment. This is what a human would do when strategizing or problem-solving toward a goal.

Examples:

On the marketing side, a programmatic advertising platforms often have a goal-based optimization system that targets CPA or ROI. 

Similarly, CleverTap’s campaign optimizer works toward set goals, for instance, increasing retention by 10%. Its goal-based agent analyzes user segments and automatically triggers a sequence of engagement campaigns (emails, push notifications, in-app offers) to hit that retention goal.

Goal-based AI agents are flexible in decision-making. They don’t follow a script and change course as long as it serves the goal. When use cases are complex with changing scenarios, marketers generally prefer them to achieve desired KPIs and outcomes. 

However, these types of AI agents are only as good as the goal’s definition and the environmental model. If goals are conflicting, the agent struggles to determine the best path forward. Moreover, goal-based AI is computationally heavy and requires more computing resources than reflex agents.

4. Utility-Based Agents

Utility-based agents measure the preference for different outcomes of an action. Instead of having a single goal state, like “achieve X at all costs”, a utility-based agent considers many possible result states and assigns a value (utility) to each, then strives to maximize that value. 

Utility-based AI agents make decision-making more nuanced when trade-offs or multiple objectives are present.

How do Utility-Based AI Agents Work? (+Example)

At the core of these agents is a utility function. It’s an algorithm that takes a state (or outcome) and returns a numerical score representing the “happiness” or usefulness of that state to the agent. 

These types of AI agents evaluate different actions based on the utility of their outcomes. Then, it selects the action that delivers the highest expected utility. It’s helpful in scenarios that involve degrees of success. Many AI systems in the real world are utility-based, even if we don’t call them that. Take the Netflix recommendation system, for example. They rank content by an estimated score. It considers factors such as past viewing, item popularity, and relevance, assigning each show a score, and then suggests the highest-scoring options. 

Utility agents are great for complex decisions with trade-offs. They have optimization in the agent’s reasoning, somewhat close to mirroring how humans make decisions in the real world. However, the tricky part is to define the utility function correctly. You need domain expertise to encode what you genuinely value and in what trade-off proportions. 

5. Learning Agents

Learning agents continually improve their performance over time by learning from their experiences. 

Unlike previous agents, which operate based on predefined logic, such as rules, models, goals, and utilities set by humans, a learning agent adapts and evolves, becoming smarter as it gathers more data.

How do Learning Agents Work? (+Examples)

A learning agent typically has four conceptual components: 

• A performance element, which makes decisions.
• A learning element that adjusts the decision-making strategy.
• A critic that evaluates the outcome against some performance standard.
• A problem generator that suggests exploratory actions.

The agent takes actions and then receives feedback or observes the result, using that information to update its internal knowledge or policy. This can be implemented via machine learning algorithms.

In marketing, you’ll see learning agents behind many advanced AI applications, for instance, in personalization and predictive analytics. 

Take CleverTap’s platform as an example; it uses AI to predict user drop-off and suggests adaptive strategies to re-engage those users. Essentially, the system learns from historical data, which patterns precede churn and what retention tactics have worked, improving its recommendations over time. 

The obvious benefit of these agents is their adaptability. However, they require a substantial amount of data and continuous feedback to ensure effective learning. 

Bonus: Other Types of AI Agents

Beyond the core agent categories above, AI agents can also be classified by the nature of their environment and interactions. 

Collaborative vs. Competitive Agents

Different types of AI agents can compete or collaborate depending on the environment. 

When two agents are trading for different firms, it represents a competitive multi-agent environment. At the same time, when two agents work in collaboration with one another toward a shared or complementary goal, it means a collaborative multi-agent environment. 

For instance, a team can have multiple customer service bots, each handling different query types while cooperating to maintain a consistent customer experience. 

As a marketer, you would find mixed scenarios in the workflow. On the programmatic advertising side, your bidding agents would compete with others for an ad slot. On the other hand, a recommendation agent and a personalization agent can work collaboratively in customer engagement workflows. 

Deterministic vs. Stochastic Agents

This actually refers to the predictability of the environment. In a deterministic environment, the outcome of an agent’s action is certain; given the current state and action, the next state is completely determined. For example, an email automation agent would send the email when triggered.

In a stochastic environment, there is randomness. The same action in the same state may lead to different outcomes. Let’s say you send a promotion to a customer (action), but the customer’s behavior toward it is unpredictable. 

It’s advisable to determine whether your environment is more deterministic or stochastic to understand the level of sophistication required in an agent’s decision-making. 

Episodic vs. Sequential Agents

This distinction comes from whether an agent’s current actions depend on past actions. In an episodic environment, the agent’s experience is divided into independent episodes. Here, each action or interaction constitutes a separate episode that doesn’t affect the next.  However, in a sequential environment, there’s a linked sequence: earlier actions influence future states and choices. 

Choosing the Correct Types of AI Agents for Your Marketing Use Case

How should you determine which type of AI agent is best suited for your needs? 

It often depends on your business size, data maturity, and specific goals. Here are some scenario-based tips for marketers making this decision:

• If you’re a small business or just starting with marketing AI: Begin with simpler agents and automate straightforward tasks with minimal setup. For example, a rule-based chatbot to answer FAQs or trigger-based email campaigns. 
• If you’re a growth-stage or mid-sized company: As your customer base and team grow, you will encounter more complex marketing challenges. Here, advanced agents like goal-based or utility-based agents would be more useful.  For instance, a goal-based agent can manage an omnichannel campaign, adjusting tactics to hit a target CPA across channels.
• If you’re an enterprise or have advanced data capabilities: Large organizations with millions of customer interactions and robust data streams should leverage learning agents and potentially multi-agent systems for maximum impact. When you have sufficient data, a learning agent uncovers patterns and micro-segments, delivering more insights. This allows you to weave hyper-personalization in your messaging while catering to numerous users at the right time. 

The stage of your company doesn’t matter as much as alignment with your goals and capabilities does. See what works for you. However, avoid the trap of using AI for AI’s sake. Leverage AI when you’re seeking efficiency gains for your team and experience benefits for customers. 

If your process is static and well-defined, traditional automation will suffice. However, if it’s complex and dynamic, you’ll need the capabilities of an agentic AI. 

Are Your AI Agents Clever Enough? 

Modern marketing respects the traditions of decades while staying current with the trends of the present and the future. The trends are moving toward autonomous and proactive AI agents that act as collaborators to human marketers. CleverTap’s Clever AI is one such technology that transforms simple, reactive systems into AI that can anticipate and orchestrate entire customer experiences. 

CleverAI utilizes a network of autonomous agents that work together to execute high-level marketing functions with minimal human intervention.

For instance, CleverAI includes: 

• Strategy Agents to help define the optimal multi-step journey to achieve a goal. 
• Decision Agents to match each user with the right message or offer
• Creative Agents to generate personalized content for a prospect or customer. 
• Action Agents to orchestrate the delivery across channels in real time.

CleverAI’s Decisioning Engine acts as the brain, ensuring the agents share context and learn from each interaction. It brings real-time context, self-learning, and scalability to every customer touchpoint. Moreover, CleverTap emphasizes trust and guardrails in this agentic approach. Marketers remain in control of branding and ethics. 

Overall, CleverAI is a robust agentic ecosystem that empowers modern marketers to truly individualize customer experiences at scale.