AI Agents vs AI Systems

The AI Solutions landscape is maturing, and there is a clear fit for almost every business. Poorly implemented, however, AI agents can eat into your ROI and leave you shaking your fist at the cloud.

In this article, we'll break down the key differences between AI Agents, AI Workflows, and AI Systems to help you make smart choices for your business.

AI Agents Explained Simply

What they are: Think of AI agents as smart software that can work independently to achieve goals and complete tasks for you. They can reason, plan, and remember things, allowing them to make decisions, learn, and adapt. Modern AI makes them even more powerful by letting them handle text, voice, video, and more all at once. 

What they can do: AI agents have several key abilities. They can operate on their own (autonomy), use logic to solve problems (reasoning), create strategies to reach goals (planning), and improve their performance over time (learning).

They can also understand their environment (perception), take actions, work with others (collaboration), remember past interactions (memory), and are driven by specific goals. They can react to changes (reactive) and also take initiative (proactive). Some can even improve themselves based on feedback (self-refining) and make smart decisions to achieve their objectives (rationality). 

Different types: There are various kinds of AI agents for different needs. Simple Reflex Agents follow basic rules, while Model-Based Reflex Agents use an internal model to make better decisions. Goal-Based Agents plan actions to reach specific goals, and Utility-Based Agents choose actions that maximise a desired outcome. Learning Agents can adapt and improve, and Autonomous Agents operate independently. In some cases, you might have Multi-Agent Systems where several agents work together, or Hierarchical Agents with a management structure.

AI Workflows: Automation with Intelligence

What they are: AI workflows are structured steps that use AI to automate and improve tasks. They combine different AI tools to process data, make decisions, and get things done more efficiently and accurately. They add intelligence to regular automation, making it more flexible. 

How they work: AI workflows usually involve collecting data, processing it, using AI to make decisions based on the data, and then acting on those decisions. They can be guided by AI agents (agentic) or follow a more set path without agents (non-agentic). 

Good for repetitive tasks: AI workflows are great for automating routine tasks that take up time and can have errors if done by humans. This frees up your team to focus on more important and creative work. Examples include things like data entry, processing invoices, sorting emails, and handling basic customer questions.  

AI Systems: The Big Picture

What they are: AI systems are a broad term for computer technologies that try to mimic human intelligence using algorithms and programs. They can include AI workflows, agents, and other AI tools working together. 

What's inside: AI systems use a variety of AI components like machine learning, deep learning, natural language processing, and computer vision. They often have different layers for data, processing, algorithms, and user interaction. How these parts are designed and work together is key to how well the AI system functions.  

How They Work Together

AI Agents and AI Workflows aren't separate ideas; they can be used together within a larger AI system. Think of AI agents as smart tools that can be used within a structured AI workflow. On the other hand, AI workflows can be used by AI agents to handle specific, often repetitive tasks. This combination lets you create AI solutions that are both flexible and controllable. 

For example, in customer service, a smart AI agent could handle many different customer questions. If a customer needs a refund, the agent could trigger a specific AI workflow designed just for processing refunds. This workflow would make sure the refund follows the correct steps, like checking the purchase, processing the transaction, and informing the customer. Here, the AI agent is the smart interface, and the AI workflow is the structured process for a specific task. This kind of setup can be visualised using tools like n8n. 

In more complex AI systems, you might have multiple AI agents working together, called multi-agent systems. These agents might have a central coordinator or work independently to handle very complex tasks that one agent or a simple workflow couldn't manage. For instance, in managing a supply chain, one AI agent might predict demand using data, while another manages inventory and coordinates with suppliers. They communicate and work together to keep the supply chain running smoothly.  

Making the Right Choice: Trade-offs to Consider

When deciding between AI agents and AI workflows, businesses need to weigh the pros and cons. AI agents offer a lot of flexibility and can handle a wide range of tasks. However, this flexibility can mean less control compared to the structured nature of AI workflows. AI agents generally have a broader scope and can handle more complex situations, while AI workflows are usually focused on a narrower set of tasks. 

Another important factor is token usage. AI agents, especially those using large language models, can consume a lot of tokens.