What are AI Agents: The Future of Intelligent Systems

With rapid advancements in artificial intelligence (AI), 2024 is poised to be a transformative year, especially with the rise of AI agents. So, what exactly are AI agents, and how do they differ from traditional AI models?

The Evolution of AI Systems

To understand AI agents, we need to first explore the evolution of generative AI systems. Traditionally, AI has relied on what we call monolithic models—large, standalone models that operate independently, trained on vast datasets to perform specific tasks. While these models can be powerful, they are inherently limited by their design and training data. Once trained, a monolithic model’s understanding of the world is set, confined to the data it was exposed to during training.

This structure poses certain challenges:

• Knowledge Constraints: The model can only draw from the data it was trained on, meaning its responses are limited to what it “knows.”
• Adaptability Issues: Adapting a model to handle new or updated information typically requires resource-intensive retraining or fine-tuning.

For example, if you wanted to use a model to check how many vacation days you have left, a standalone model would struggle with this task. It wouldn’t have access to specific personal data, nor would it be able to pull in new information without additional programming. This is where the concept of compound AI systems comes in.

Introducing Compound AI Systems

A compound AI system differs from monolithic models by integrating multiple components or “modules” to create a more dynamic, flexible system. Rather than relying on a single model, compound systems combine various AI models, databases, tools, and processes to handle complex tasks. They’re more adaptable than traditional models and can be designed to access real-time data, retrieve information from databases, and respond with greater accuracy to personalized queries.

In the vacation example, a compound AI system could connect to your company’s HR database to retrieve your remaining vacation days. Here’s how it works in steps:

1. Query Submission: You input your question about vacation days.
2. Processing: The AI model interprets the query and initiates a search of the relevant database.
3. Data Retrieval: The system retrieves the specific information (e.g., remaining vacation days) from the HR database.
4. Response Generation: The model then crafts a personalized response based on the retrieved data.

This modular approach makes it possible to build tailored AI solutions that incorporate specific business logic and external data sources, increasing the model’s effectiveness and accuracy for real-world tasks.

The Modular Design of Compound AI Systems

Compound AI systems are inherently modular, meaning they’re composed of various interchangeable components. This modularity offers a high degree of flexibility, allowing developers to combine different tools and models to achieve desired functionality. The key components often include:

• Models: These can be large language models, image generators, or other AI models suited to specific tasks.
• Programmatic Components: Additional functions or programs can be integrated around the AI model to handle specialized tasks.
• Tools and Verifiers: External tools (like search engines, calculators, or API integrations) can be included to assist in verifying information, conducting searches, or breaking down complex queries.

When designing a compound system, developers can select the components that best match the task requirements. This approach simplifies complex problem-solving by creating workflows that rely on specialized components rather than a single, all-encompassing model.

For instance, Retrieval-Augmented Generation (RAG) is a popular type of compound AI system that combines a language model with retrieval capabilities. In a RAG setup, the language model is able to query an external data source to produce more accurate and contextually relevant answers. This architecture is widely used because it’s efficient, adaptable, and can be tailored to many applications. However, RAG systems follow a specific logic path, or control logic, meaning they are only as flexible as their design permits.

The Concept of Control Logic

A fundamental aspect of compound AI systems is control logic—the predefined pathways that a system follows to produce a response. Control logic is programmed by developers to specify how the system should handle different types of queries. While effective, this setup has limitations. Because compound systems are designed to follow specific paths, they may falter if asked a question that lies outside the defined scope.

Consider the vacation days example again. If you suddenly asked the compound system about the weather, it would likely fail, as its control logic is set up to query a vacation database rather than a weather database. This example illustrates the constraint of rigid control logic, which works well for narrow, well-defined tasks but can lack flexibility when responding to broader, more variable queries.

The Shift Toward Agentic AI Systems

AI agents represent a new approach that addresses some of the limitations of control logic. Instead of defining every path a system might take, agentic AI places a large language model (LLM) at the core of the control logic, allowing it to act more autonomously. This new structure allows the model to generate a response plan, approach each task incrementally, and adapt dynamically if it encounters obstacles. AI agents mark a shift from rigid programming to more flexible, human-like reasoning.

In traditional systems, the AI is instructed to “think fast”—producing a quick answer without much introspection. By contrast, an AI agent can “think slow,” carefully analyzing complex questions, breaking them down, and revisiting any part of the process as needed. This capability makes AI agents well-suited for multi-step tasks where adaptability and iterative problem-solving are crucial.

The Structure and Capabilities of AI Agents

When we talk about AI agents, we’re discussing a model that not only interprets and generates responses but also manages control logic on its own, thanks to advancements in large language models (LLMs). AI agents have three primary capabilities that allow them to handle complex tasks in ways that traditional systems cannot:

1. Reasoning: This is the core of agentic AI. The agent doesn’t simply respond to queries; it actively thinks through problems, often breaking them into smaller tasks and tackling each one individually. By prompting the model to approach each step thoughtfully, the agent can develop a multi-step solution path to tackle complex issues.
2. Acting (Tool Use): AI agents can call on various external tools as needed. These tools might include a search engine for fetching information, a calculator for mathematical tasks, or even other specialized models for tasks like translation. Known as tool use, this capability allows the agent to perform tasks that extend beyond its initial programming. For instance, if a query involves mathematical calculations, the agent can call on a calculator tool, or if it needs to gather information from the web, it can initiate a search.
3. Memory: Memory enables agents to retain contextual information over time, allowing them to remember details from previous interactions. This could mean recalling past questions to create continuity in conversation or logging internal steps to track its reasoning process. This stored information makes future interactions more personalized and insightful.

Together, these capabilities allow AI agents to be far more flexible and adaptive than traditional compound AI systems, positioning them to handle complex, multi-step tasks with greater autonomy.

The REACT Framework for AI Agents

A popular framework for configuring AI agents is REACT, which stands for Reasoning and Acting. In this setup, the agent doesn’t just provide an immediate answer; instead, it carefully plans its response. Here’s a step-by-step look at how a REACT agent processes a user query:

1. User Query: The user’s question is input into the system, prompting the agent to assess the request.
2. Reasoning Process: Instead of delivering a quick, surface-level answer, the agent is instructed to “think slow” and develop a strategic plan for addressing the question.
3. Tool Use (Acting): As it follows the planned steps, the agent can determine if it needs help from external tools. For example, if the question requires data not stored in memory or complex calculations, the agent will call on tools to complete those steps.
4. Observation and Adjustment: The agent then evaluates whether the responses from the tools are useful or if there’s an error or gap. If needed, it will revise its plan and iterate until it arrives at a satisfactory answer.

Through this process, the agent combines reasoning, tool use, and adaptability, allowing it to respond thoughtfully to multi-step queries. This contrasts with systems that rely solely on static pathways or rigid control logic.

Real-World Example: Planning a Vacation with a REACT Agent

To see how a REACT agent might work, imagine you’re planning a vacation to Florida and want to calculate how many bottles of sunscreen to bring. Here’s how the agent could handle this complex query:

1. Retrieve Past Data: The agent remembers your previous queries and retrieves data about your remaining vacation days.
2. Weather Research: Knowing you’ll be in Florida, it accesses a weather forecast database to estimate hours of sun exposure.
3. Health Guidelines: The agent might consult a health website to get information on the recommended sunscreen dosage based on hours spent in the sun.
4. Mathematical Calculation: It uses a calculator tool to estimate how many two-ounce bottles of sunscreen you’ll need, factoring in your planned hours in the sun and the recommended dosage.

This multi-step approach allows the agent to pull together diverse information sources and execute a solution. Notably, this adaptability is possible because of the REACT framework, which guides the agent to think, plan, and act as needed.

The Sliding Scale of AI Autonomy

A critical factor in designing AI systems is determining how much autonomy to give the agent. Different tasks require varying levels of AI independence:

• Narrow Tasks: For well-defined, specific tasks (like looking up vacation policy data), a simple programmatic system may be more efficient. These narrow tasks don’t require much flexibility, so a compound system with set control logic is often sufficient.
• Complex Tasks: For broader, complex tasks, an agentic approach is more suitable. When an agent handles diverse queries (like a support bot that addresses multiple issues), rigid programming could limit its effectiveness. Here, agentic autonomy enables the system to flexibly adapt to a range of questions, reducing the need to program each potential response path manually.

For example, if an AI system is expected to troubleshoot various issues on GitHub, it would require the agent’s ability to reason through different problems, check relevant codebases, and adapt its approach based on the nature of each issue.

The Future of AI Agents: Combining System Design and Agentic Intelligence

We are witnessing a merging of two powerful trends in AI: system design (combining various tools and models) and agentic intelligence (allowing the AI to reason and adapt). This hybrid approach gives us more intelligent and versatile systems that can be deployed in numerous applications. The future of AI agents lies in this combination, where modular system components and intelligent reasoning capabilities come together to create solutions that are flexible, scalable, and more human-like in problem-solving.

In 2024, we can expect to see more agent-based AI systems, with human oversight gradually decreasing as accuracy improves. While some applications will still require humans in the loop to verify outputs, many routine and complex tasks will be increasingly handled by AI agents capable of learning, adapting, and responding independently.

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Zachary Skinner

Zachary Skinner is the editor of TechDrivePlay.com, where tech, cars and adventure share the fast lane.
A former snowboarding pro and programmer, he brings both creative flair and technical know-how to his reviews. From high-performance cars to clever gadgets, he explores how innovation shapes the way we move, connect and live.