Artificial intelligence (AI) agents are taking business by storm. These smart tools are capable of making autonomous decisions that support both customers and employees.
While they may sound complex, there’s certainly no need to be fully versed in the underlying mechanics of AI to harness an agent effectively — you just need a basic understanding of what an agent is and how they work.
In this article, we take a closer look at agent architecture in AI, including its components, the common architecture types that exist, and the use cases of AI architecture across various industries, such as healthcare and education.
What is an AI agent?
Simply put, an AI agent is a program or software that’s capable of autonomously performing tasks — from natural language processing, to decision-making, to problem-solving, and much more. They’re designed to accomplish a predetermined goal without much human interaction or prompting, increasing a business’s efficiency and productivity while improving task accuracy.
For instance, in the case of an e-commerce business, an AI agent may be deployed to track online orders, provide shipping updates to customers, and escalate any problems to management. Other examples of AI agents are scheduling tools, appointment notification tools, and form-filling tools.
What is agent architecture in AI?
Agent architecture in AI refers to the structural components of AI agents, including how they interact with each other and other types of software. The architecture defines the best possible way for the agent to reach its objectives and complete its goals.
There are a number of different types of agent architecture in AI, including reactive, deliberate reasoning, and layered/hybrid architectures.
What are the different types of agent architectures?
Here’s a brief overview of the different types of agent architectures that AI agents use:
- Reactive architectures: This type of architecture is the simplest. Reactive AI agents are designed to complete a straightforward task — they aren’t focused on achieving long-term or complex goals but simply respond to the changes they perceive in their environment through various stimuli.
- Deliberative reasoning architectures: These agents have an internal symbolic model of the world and are designed to make decisions to reach their goal using any combination of facts, rules, memory, pattern, and logic. They are best at achieving long-term goals as they take time to plan and execute complex instructions.
- Layered/hybrid architectures: Layered or hybrid AI agent architectures are more complex than the previous two. They are made up of an internal hierarchy with layers of function, which contain both reactive and deliberative reasoning components. These AI agents can achieve both simple tasks and long-term goals at the same time.
5 core components of agentic AI architectures
While each type of AI agent is unique, most architectures are built with similar mechanisms and capabilities:
- Perception: AI agents are equipped with the capability to sense and interpret their environment in relation to the goal they are programmed to achieve. The module processes relevant data — such as numerical values, text, and images or videos — through digital databases, cameras, and sensors.
- Reasoning: AI agents have complex decision-making processes based on internal models or rules within the system. This is how the AI agent “thinks” about the steps needed to achieve its goal.
- Learning: The agent can learn from its previous interactions with other software systems and humans, and use that knowledge to inform its future interactions. For example, if the agent takes an incorrect action and receives a penalty, it will keep that in mind the next time it’s faced with a similar task.
- Action: AI agents must take various physical or virtual actions to complete their objective. The action component uses tools like motors, speakers, and network interfaces to direct the agent to specific steps towards achieving its goal.
- Knowledge: What sets many AI agents apart from other types of similar technology is that they have a memory and contextual understanding of their world. The knowledge component contains information about the AI agent’s environment in relation to its goals.
What are the advantages and disadvantages of reactive agent architectures?
Of the three types of agent architecture in AI, reactive is the most common. Since these agents are fairly basic and well suited to a variety of tasks (such as booking meetings, sending reminders, scheduling work shifts, and other admin), organizations of all types are starting to take advantage of them regularly. However, there are also some negatives to account for:
| Pros | Cons |
|---|---|
| Easy to develop and implement | Aren’t capable of considering long-term goals |
| Highly responsive to stimuli, making them ideal for simple, repetitive tasks | Can’t take information outside of their specific goal into account |
| Don’t require many resources to run, or constant maintenance or management, making them ideal for small tech teams | Can be difficult to debug should a problem occur |
| Easy to scale when workloads increase | Still need to be monitored for safety, ethicality, and accuracy |
Ultimately, while AI agents are fairly easy to install and implement, you should consider whether they can fully meet your needs. You may also still need to dedicate resources to them.
Agent architecture in AI: Applications across various industries
Agentic AI can be applied across several different industries. Here are a few areas it’s commonly being used in now:
- Hospice: In this sensitive healthcare sector, a Hospice Care Coordinator Agent can assist relatives of patients in filling out necessary forms and streamlining admin during stressful times.
- Finance: AI agents can support the finance industry through predictive analysis or fraud detection. They can also optimize customer relationships by ensuring customer data is up to date.
- Real estate: Real estate is admin heavy. A Real Estate Consultant AI agent can assist clients in securing their dream property by helping them fill out forms, sending contract notifications, and more.
- Customer service: AI agents are great at interacting with customers because they can answer simple queries and respond outside of normal business hours. They can also be used to schedule appointments with sales reps.
- Education: A School Administrator Agent can provide communication that fosters a strong partnership between students and parents. The AI agent can automate various administrative tasks within educational institutions, such as sending notifications and reminders, filling out forms, and more.
Challenges in implementing agentic AI architecture systems
While agentic AI systems can certainly benefit many organizations and industries, it’s important to remember that these systems don’t come without their challenges — from implementation to proper management.
For one, you may need to dedicate IT resources to maintain even the simplest agentic AI solution in order to resolve issues from time to time. Security is also a concern for many organizations, so it’s vital to prevent unauthorized access to the system to ensure your data remains safe.
Depending on your industry — especially if it’s a highly regulated one like healthcare or finance — you’ll also want to make sure that any technology you use meets the appropriate regulations. Similarly, consider any concerns surrounding diversity, equality, and inclusivity.
Photo by Vojtech Okenka
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