Publish Date

January 24, 2025

7 Types of AI Agents in 2025 and Beyond

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From chatbots that answer questions in real-time to digital replicas of factories predicting equipment failures, AI agents are everywhere, “loudly” transforming industries and our daily lives. But not all AI is the same.

This article breaks down each type of AI agent, showing you how they work, who they’re best for, and how they’re changing the world one innovation at a time.

What are the different types of AI agents

Here are the seven main categories of AI agents:

  1. Large Language Model (LLM)-based agents
  2. Multi-modal agents
  3. Compound AI systems
  4. Autonomous research agents
  5. Digital twin agents
  6. Interactive emotional agents
  7. Swarm intelligence agents

1. Large Language Model (LLM)-based agents

LLM-based agents, like Tina, are powered by advanced neural networks (i.e., OpenAI's GPT-4, Google's LaMDA, and Meta's LLaMA**)** designed to understand and generate human language.

An in-depth guide into LLM-based agents. Source: X

Architecture and how it works

Their architecture starts with tokenization, where your “prompt” is broken down into smaller units called tokens (such as words or parts of words) that the model can process.

Large language model (LLM)-based agents' architecture. Source: MC² Finance

These tokens are then passed through an embedding layer, which converts them into numerical representations that capture their meanings.

💡 For example, a question like “What is the price of Bitcoin?” is split into tokens like ["What", "is", "the", "price", "of", "Bitcoin", "?"]. Words with similar meanings, like “crypto,” “Bitcoin,” and “blockchain,” are placed close to each other in a high-dimensional space, allowing the model to understand relationships between words.
The closer the words are, the more similar their meanings, helping AI models understand relationships between concepts. Source: ResearchGate

After this, the attention mechanism comes into play. It’s like the agent’s way of focusing on the most relevant parts of the input text.

💡 For example, in a sentence like “Bitcoin is rising in price,” the model pays more attention to “Bitcoin,” “rising,” and “price” to understand the context.

Next, the data flows through transformer layers, which are the backbone of the model. These layers refine the understanding of the input by processing it multiple times, capturing complex patterns, grammar, and meaning. Once the input has been fully analyzed, the output layers generate a response.

💡 For example, if you ask Tina about a token’s performance on MC² Finance App or Discord, she combines her understanding of the question with real-time data to craft a clear and accurate answer.

LLM-based agents are also often fine-tuned on specific datasets to specialize in particular areas (like cryptotrading or key whale wallet alerts).

💡 For instance, Tina is trained on MC² Finance's datasets to understand token prices, market caps, and other crypto-specific details. From token performance statistics (TPS) and crypto ETPs to top tokens on each chain, Tina delivers insights not found anywhere else.

What makes LLM-based agents useful?

What makes LLM-based agents truly powerful is their ability to learn and improve. Through feedback loops, they adapt based on user interactions, ensuring their responses stay relevant and accurate over time.

💡 This architecture allows Tina to function as an intelligent, conversational assistant who can handle both simple queries and complex financial questions, making her invaluable for traders and crypto enthusiasts alike.
LLM-based agents best for whom?

Anyone needing conversational AI with domain-specific knowledge, such as customer support, financial analysis, or personal assistance.

Worthy mention

Tina’s X account provides “not-to-miss-out-on” updates on key crypto whale moves and news.

2. Multi-modal agents

Multi-modal agents are designed to process and combine different types of data—such as text, images, and audio—into a unified understanding, making them capable of solving complex problems.

A research paper that goes deep into multi-modal AI agents for automated crypto portfolio management. Source: X

Architecture and how it works

Their architecture begins with feature extraction, where each type of input data (e.g., text from news articles, images of price charts, or audio from expert interviews) is analyzed separately.

💡 For example, if you input an image and a caption, the image is analyzed to identify objects, colors, or patterns, while the text is broken down into meaningful tokens like words or phrases. This ensures that the agent understands each type of data in its native format.
Architecture of multi-modal agents. Source: MC² Finance

Once the features are extracted, they are passed through a fusion layer, which combines the different data streams into a single, cohesive representation. This is where the agent brings together the visual and textual inputs, understanding how they relate to each other.

💡 For example, if the input is a picture of a cat with the caption “A playful kitten,” the fusion layer links the image of the cat with the descriptive text to create a richer understanding.

To ensure the agent processes all inputs contextually, it uses a cross-attention mechanism. This allows the agent to focus on relevant parts of one input in relation to another.

💡 In the case of the cat example, the agent might focus on the word “kitten” while analyzing the image, ensuring that it understands the subject is a young cat.

After merging and processing the inputs, the output layer generates a response that incorporates all the information.

💡 For instance, a multi-modal agent might describe the cat’s appearance in detail or generate a story based on the image and caption.

What makes multi-modal agents useful?

What makes multi-modal agents unique is their ability to handle a wide variety of tasks by blending different data formats. They are often trained on large datasets containing paired data, like images with captions or videos with subtitles, allowing them to learn how different modalities interact.

How a multi-modal AI system (like GPT-4 Vision) processes various types of data, such as landmarks, art, and text. Source: AIModels.fyi
Multi-modal agents best for whom?

Creative industries (e.g., graphic designers) and tasks involving multiple data formats.

Worthy mention

DALL-E, which creates images from text descriptions.

3. Compound AI systems

Instead of relying on a single AI model, these systems combine multiple models (e.g., a sentiment analysis model for text, a convolutional neural network [CNN] for image recognition, and a speech-to-text model for audio processing), each designed for a specific task, into one coordinated architecture.

Prompting LLMs for general tasks vs building compound AI agents. Source: X

Architecture and how it works

Their design begins with a data collector, which gathers raw information from various sources, such as APIs, sensors, or databases.

💡 For example, in a financial application, the data collector might pull real-time market prices, news articles, and trading histories.
Architecture of compound AI system. Source: MC² Finance

Once the data is collected, it moves to the analyzer, which processes and interprets it. This could involve natural language processing to understand news articles, statistical models to analyze market trends, or visual processing to interpret graphs.

💡 The key is that each analyzer specializes in one type of task, ensuring accurate and efficient processing.

After the data is analyzed, the decision-maker comes into play. This component evaluates the results from all analyzers and determines the best course of action.

💡 For instance, in a trading scenario, the decision-maker might identify a profitable trade based on current market conditions and past trends.

The final step is the executor, which acts on the decision. In a trading system, this could mean placing a buy or sell order directly on an exchange. Each part of the system communicates seamlessly with the others, ensuring that decisions are based on the best possible insights from all data sources.

💡 While Tina specializes in answering user queries and providing real-time token data, she could be integrated with other agents that handle market prediction or execute trades. Together, they form a complete financial ecosystem, enabling users to analyze, decide, and act—all within a single platform.

What makes compound AI agents useful?

Compound AI systems are particularly powerful because they allow for modularity and flexibility. Each component can be improved or replaced without affecting the entire system.

Components of compound AI systems. Source: The Technomist
Compound AI agents best for whom?

Businesses or researchers managing large-scale, multi-step projects.

Worthy mention

Custom-built AI systems tailored to the specific needs of industries like healthcare or finance.

4. Autonomous research agents

Autonomous research agents are designed to independently conduct experiments, analyze data, and generate new insights without constant human intervention, functioning as self-sufficient scientists.

A research paper that goes into using autonomous research agents as lab assistance. Source: X

Architecture and how it works

Their architecture integrates several specialized components (e.g., data analysis modules for processing experimental results, hypothesis generation models for proposing testable ideas, and simulation engines for running virtual experiments) to ensure they can perform every step of the research cycle efficiently.

Architecture of autonomous research agents. Source: MC² Finance

The process begins with a knowledge base, which serves as the agent’s repository of prior information. This database includes past experiments, research papers, and any other relevant data the agent might need to understand the problem.

💡 For example, in drug discovery, the knowledge base might include chemical properties, molecular structures, and the results of previous experiments.

The next component is the inference engine, which is responsible for formulating hypotheses. Using the data from the knowledge base, the agent identifies patterns, gaps, or unexplored areas.

💡 For instance, in material science, the agent might hypothesize that a specific combination of materials could result in stronger alloys. This step is crucial because it allows the agent to proactively explore new ideas rather than waiting for human direction.

Once a hypothesis is generated, the agent uses a simulation environment to test it. This environment models real-world scenarios virtually, enabling the agent to conduct experiments quickly and at a low cost.

💡 For example, in climate modeling, an autonomous research agent might simulate various weather patterns to predict the impact of rising temperatures on ecosystems.

After completing the simulations, the agent moves to the analysis phase, where it evaluates the results of its experiments. Advanced statistical tools and machine learning models are used to determine whether the hypothesis was successful or needs refinement.

💡 For example, if the agent is working on identifying potential treatments for a disease, it might analyze which compounds showed the best results in simulations.

Finally, the agent enters the feedback loop, where it refines its approach based on the findings. If a hypothesis proves successful, the agent adds the new information to its knowledge base, ensuring that future experiments build on these results. If the hypothesis fails, the agent adjusts its parameters and tries again, learning from its mistakes.

💡 For instance, Tina will evolve into an autonomous research agent by generating hypotheses about token performance trends, running simulations on historical market data, and providing actionable insights to traders.
Taxonomy of autonomous agents. Source: ResearchGate
Autonomous research agents best for whom?

Scientists and researchers who want to speed up discovery.

Worthy mention

Atomwise.

5. Digital twin agents

Digital twin agents are virtual replicas of physical systems, processes, or objects that simulate real-world behavior in a digital environment. They continuously update based on real-time data from their physical counterparts, enabling them to monitor, predict, and optimize performance.

Agent simulations to create digital twins of cities. Source: X

Architecture and how it works

Their architecture integrates several components to ensure they replicate reality as accurately as possible while providing actionable insights.

Architecture of digital twin agents. Source: MC² Finance

The process starts with data sensors embedded in the physical system. These sensors collect real-time data such as temperature, pressure, speed, or any other parameter relevant to the system.

💡 For example, in a wind turbine, sensors might measure wind speed, blade rotation, and energy output. This data is then transmitted to the digital twin agent.

Once the data is collected, it flows into the virtual model, a digital simulation of the physical system. This model is built using advanced physics-based algorithms and machine learning models, ensuring it accurately mirrors the behavior and characteristics of its real-world counterpart.

💡 In the case of the wind turbine, the virtual model would simulate how the turbine operates under different wind conditions.

The next component is predictive analytics, which allows the digital twin to forecast future performance or identify potential issues. Using the real-time data from sensors and historical data from past operations, the agent employs predictive models to anticipate outcomes.

💡 For instance, it might predict when a component in the wind turbine is likely to fail, enabling proactive maintenance.

The feedback mechanism is a key part of the architecture. It enables the digital twin agent to send recommendations or actions back to the physical system.

💡 For example, if the digital twin detects inefficiencies in the turbine's operation, it might suggest adjustments to optimize energy production. This two-way communication ensures that the digital twin doesn’t just observe but actively helps improve performance.

Digital twin agents are also equipped with visualization tools, which present their findings in an easily understandable format. These tools allow users to view simulations, analyze data trends, and make informed decisions.

💡 For instance, a digital twin managing a factory might display a real-time dashboard showing equipment status, productivity metrics, and predicted maintenance needs.

What makes digital twin agents useful?

By providing a dynamic, virtual representation of physical systems, digital twin agents bridge the gap between the real and digital worlds, empowering you to achieve greater control and insight into complex processes.

Application of digital twins and metaverse. Source: MDPI
Digital twin agents best for whom?

Engineers and managers in industries like manufacturing, energy, or urban planning.

Worthy mention

Siemens’ digital twin solutions, widely used in industrial applications.

6. Interactive emotional agents

Interactive emotional agents are AI systems designed to recognize, interpret, and respond to human emotions. These agents simulate empathy and emotional intelligence, allowing them to engage in more natural and meaningful interactions.

What agentic interaction in crypto will evolve into. Source: X

Architecture and how it works

Their architecture combines several advanced components (e.g., sentiment analysis algorithms for text input, facial expression recognition using computer vision, and vocal tone analysis through audio processing) to detect emotions and adapt responses accordingly

Architecture of interactive emotional agents. Source: MC² Finance

The process begins with emotion detection, which relies on various inputs such as text, speech, and facial expressions.

💡 For instance, a chatbot might analyze the tone of a user’s message, while a virtual assistant with a camera can interpret facial cues like smiles or frowns.

Sentiment analysis tools are often employed to assess emotional tones in text, categorizing them as positive, negative, or neutral.

💡 In the case of voice-based interactions, acoustic features such as pitch, volume, and rhythm help the agent infer emotions like happiness, anger, or sadness.

Once the emotions are detected, the agent uses context awareness to understand the situation. This involves analyzing the content of the conversation and the user’s emotional state together.

💡 For example, if a user says, “I was busy trading today,” with a tone of sadness, the agent understands the emotion better than most people. This step ensures the agent’s responses are appropriate and empathetic.

The next step is adaptive response generation, where the agent crafts a reply tailored to the user’s emotional state and context. This involves selecting the right tone, words, and even timing to convey empathy or encouragement.

💡 For instance, an emotional support chatbot might respond to a stressed user by saying, “Markets been tough, eh?” and then provide good portfolios to follow on MC² Finance, in comparison with his part performance and average performance seen across chains (or entire industries even).

Interactive emotional agents often include a feedback mechanism to learn and improve over time. By analyzing how users react to their responses, these agents refine their ability to detect and respond to emotions.

💡 For example, if users consistently respond positively to certain phrases or tones, the agent prioritizes those in future interactions.

These agents also integrate personality modeling to make interactions feel more human. By maintaining consistency in their tone and style, they can build trust and rapport with users.

What makes interactive emotional agents useful?

Interactive emotional agents help you feel heard and understood, fostering stronger connections between people and technology. By combining emotion detection, context awareness, and adaptive responses, these agents make interactions not just functional, but genuinely meaningful and valuable.

How intelligent AI agents predict emotional empathy? Source: MDPI
Interactive emotional agents best for whom?

Mental health support, customer service, or teaching tools that need a human touch.

Worthy mention

AI tools like Wysa, designed to improve emotional well-being.

7. Swarm intelligence agents

Swarm intelligence agents are inspired by the behavior of natural systems like ant colonies, bird flocks, or bee hives. These agents work collectively in a decentralized manner to solve complex problems by leveraging simple rules and interactions.

Swarm intelligence: Where it all started. Source: X

Architecture and how it works

Their architecture is designed to mimic nature’s ability to coordinate large groups of individuals without central control, enabling efficient problem-solving through collaboration.

Architecture of swarm intelligence agents. Source: MC² Finance

The process begins with local decision-making, where each agent operates independently based on its immediate environment and local data.

💡 For example, in a search-and-rescue operation, a drone might detect debris in one area and prioritize searching nearby zones for survivors. These decisions are made using simple rules, such as moving toward a target or avoiding obstacles.

Next comes communication through signals, which allows agents to share information with each other. In nature, ants leave pheromone trails to guide others to food sources. In swarm intelligence systems, this communication is typically digital.

💡 For example, robots in a warehouse might share locations of available inventory through wireless signals, enabling them to collectively optimize the retrieval process.

The emergent behavior of the system arises from the interactions between agents. While individual agents follow simple rules, their collective behavior leads to complex outcomes.

💡 For instance, a group of drones might cover an entire disaster area efficiently by dynamically adjusting their paths to avoid overlapping with others, ensuring maximum coverage without central coordination.

A critical component of swarm intelligence is real-time adaptability. As agents receive new information, they update their strategies and adapt to changes in the environment.

💡 For example, if one drone in a swarm encounters a barrier, it can reroute and signal the others to avoid that area, ensuring the team’s overall mission remains on track.

Another key feature is robustness and scalability. Since swarm systems lack a single point of failure, they remain effective even if some agents are lost or malfunction. Additionally, adding more agents enhances the system’s capabilities without requiring major changes to its architecture.

💡 Imagine a swarm intelligence system applied to cryptocurrency trading. Each agent in the swarm could monitor specific market segments, such as token performance, whale movements, or buying pressure. The agents would communicate findings, allowing the swarm to identify patterns and make collective decisions about trading strategies.

What makes Swarm intelligence agents useful?

Swarm intelligence agents are particularly useful in scenarios requiring large-scale coordination and problem-solving, such as logistics, environmental monitoring, or disaster recovery. By combining local decision-making, real-time communication, and adaptive strategies, these agents can tackle challenges that are too complex or dynamic for a single system to manage.

Three characteristics for swarm intelligence agents. Source: ResearchGate
Swarm agents best for whom?

Large-scale, distributed tasks like search-and-rescue or logistics.

Worthy mention

Robobees.

Picking the right agent

Choosing the best AI agent depends on your needs:

Types of AI agents with examples. Source: MC² Finance

For everyday tasks: LLM-based agents like ChatGPT.

For creative projects: Multi-modal agents like DALL-E.

For big challenges: Compound AI systems for their versatility.

For scientific breakthroughs: Autonomous research agents.

For real-time monitoring: Digital twins.

For emotional connection: Interactive emotional agents.

For teamwork in action: Swarm intelligence agents.

Final thoughts

By understanding their architectures and purposes, you can decide which type fits your needs best—and maybe even imagine how they can change the world.