AI Agents: The Complete Guide for Any Business

Organizational and business leaders can no longer escape AI.

In fact, 75% of employees are now using AI at work.

Sure, it’s a game-changer for individual efficiency, but is that all it has to offer? Could AI bring even bigger benefits to your team and drive more real business impact?

That’s where the AI agent comes in. Far from being just another tool, an AI agent is like having a highly capable, tireless teammate who supports your team. It can provide insights, enhance creativity, and even anticipate needs, helping your business stay competitive and innovative in a fast-evolving landscape. AI agents might sound complex. But the truth is you’re already using them every day, even at home.

In this article, we’ll discuss:

• What are AI agents ?
• How working with agents is different from non-agentic AI ?
• Five types of AI agents 
• How to start creating AI agents. 

By the end of this article, you’ll know exactly what AI agents are, and if agentic AI is a solution you want to implement in your company. 

What are AI agents?

AI agents are autonomous systems that use artificial intelligence to detect their environment, process data, and take action without regular human input. 

Just as a skilled chef can improvise a meal based on available ingredients and preferences, an AI agent can adapt to new situations and solve problems. 

AI agents are utilized across various use cases, such as:

• Supporting contact centers with tasks like data retrieval and issue escalation
• Automating sales and marketing with report generation
• Enhancing customer engagement through request classification
• Boosting productivity via document distribution
• Powering chat interfaces for seamless interactions.

We'll also cover some rather surprising examples of AI agents a bit later in this article.

Agentic vs Non-agentic AI

The key factor to agentic AI is that it can make decisions on its own. Non-agentic AI, on the other hand, doesn’t have the same autonomy. It only functions and provides outputs when someone provides an input. 

Think of it like this: when you ask ChatGPT to analyze your most recent profit and loss statement, it can provide tailored suggestions based on the information you give it and what it has learned during training. However, agentic AI goes a step further—it can independently locate the relevant data, decide what analysis is needed, and deliver a fully customized report without requiring step-by-step instructions from you every time.

Here are some more detailed differences between agentic and non-agentic AI:

Goal-oriented

Agentic AI has specific goals and can plan to achieve them, whereas non-agentic AI responds to immediate inputs. A good example of goal-oriented AI is a smart inventory system. 

Amazon has reportedly been working on a smart fridge. It can detect if certain items are low and order them on Amazon.

Learning and Adaptation

Agentic AI continuously improves its performance and knowledge. It analyzes past actions, incorporates new information, and identifies patterns to enhance decision-making and predictions. This ongoing learning enables agentic AI to become more sophisticated and effective across various tasks and environments.

For example, an AI-powered financial advisor could proactively alert clients about market changes and suggest portfolio adjustments rather than waiting for the client to request advice.

Proactivity

Agentic AI can initiate actions independently, while non-agentic AI is reactive to user commands or predefined triggers.

For example, an AI-powered personal assistant can proactively suggest schedule changes based on traffic conditions or upcoming weather events, while a traditional digital calendar simply displays events as the user enters them. 

Environment Interaction

Agentic AI can perceive and interact with its environment, whereas non-agentic AI often operates in a more limited context. For instance, an autonomous robot in a warehouse can navigate its surroundings, identify and pick up items, and adapt to changes in the layout. 

Complexity

Agentic AI systems are generally more complex, incorporating decision-making algorithms and often multiple AI technologies.

The most popular example of a complex AI agent system is, of course, the self-driving car. It uses various hardware and software systems to navigate complex traffic scenarios without a human driver being involved in the input. 

ChatGPT: AI Agent or Not?

ChatGPT is not technically an AI agent because it lacks autonomy and doesn’t have planning capabilities. It constantly relies on prompts to provide an output. Plus, it also doesn’t have a specific goal. The goal always depends on the user's desire for a specific chat session. 

For example, you can ask ChatGPT for advice on the best way to accomplish your daily to-do lists. But it won’t generate an efficient task list based on past data and external sources like your calendar and your company's CRM data.

However, ChatGPT does exhibit some agent-like qualities, like understanding context and generating human-like text (through natural language processing or NLP).

What are the Five Types of AI Agents?

AI agents can perform potentially thousands of tasks, but most of them fall into the following five categories.

Simple Reflex

Simple reflex is exactly how it sounds. It works like this:

1. It has a set of pre-programmed rules.
2. When something happens, it checks these rules.
3. It reacts based on the matching rule.

These agents don't have memory or the ability to learn. They're similar to simple robots that can only do exactly what they're told. They react quickly but can only handle situations for which they've been programmed.

Let’s take an indoor air quality monitoring system as an example. When the dust particle level exceeds a certain threshold, it triggers a pre-programmed rule: "If the PM2.5 level is high, activate the air purifier and turn on the robot vacuum."

In this case, the system detects high PM2.5 levels and automatically turns on the air purifier and the vacuum to start cleaning.

Model-based Reflex

The next type is model-based, an upgraded version of a simple reflex. The main difference is that model-based AI agents remember past data and actions.

A model-based reflex AI agent is like a smart thermostat that remembers things. It uses two types of information to make decisions:

1. Current data from its sensors
2. Memories of what happened before.

For example, it can adjust the temperature in your home based on the current temperature and how you’ve set it in the past. Then, it chooses the best temperature based on that collection of data.

Goal-based AI Agents

As the name suggests, goal-based AI agents have a specific goal to achieve. 

For example, Asana AI demonstrates a goal-based agent in action. When given a project goal like "Launch Q1 Marketing Campaign," it automatically creates task dependencies, sets milestones, and assigns work based on team members' current workload.

This intelligent adaptation helps teams stay on track while responding to common workplace challenges like sick leave, shifting priorities, or resource constraints. With Asana, you can design your AI agents to support whatever goals you decide. 

Utility-based Agents

Utility-based agents try to make the best choices. They look at different options and pick the one that gives the best result.

For example, an AI agent in HR recruitment continuously optimizes candidate screening and selection. The agent evaluates different recruitment strategies by considering factors like time-to-hire, quality of hire, cost-per-hire, and candidate experience scores.

When screening resumes, the agent might choose between different approaches:

 • Prioritizing candidates with exact skill matches

 • Considering transferable skills from different industries

 • Weighing cultural fit indicators 

• Factoring in retention probability based on historical data

The agent selects the strategy that maximizes the overall utility–balancing the need to fill positions quickly with the quality and longevity of potential hires. It might fast-track certain candidates during peak hiring seasons while being more selective when there's less urgency.

Learning Agent

A learning AI agent represents the most sophisticated form of artificial intelligence because of its ability to improve through experience and data analysis continuously. Unlike simpler AI systems that follow fixed rules, learning agents adapt their behavior based on outcomes, develop new strategies through pattern recognition, and autonomously refine their decision-making processes. 

Salesforce's Einstein AI for SDRs is a great example. It works by watching and learning from everything sales reps do–their emails, phone calls, and how well their sales efforts work. The system then uses this information to work such as:

• Figure out which sales leads are most likely to convert into paid customers
• Suggest the best times to contact customers
• Create better email templates
• and help sales reps know what to do next.

As sales reps use the system, it gets smarter and more helpful. It learns what works and what doesn't, then adjusts its suggestions to help reps close more sales and work more efficiently.

Is an AI Agent the Right Solution?

An AI agent can be the right solution if your business needs to automate complex, data-driven tasks that require adaptive decision-making and continuous learning.

When considering whether an AI agent is the right solution for your business, it's important to evaluate several factors:

• Task complexity: AI agents are ideal for handling complex, repetitive tasks that require decision-making based on multiple factors.
• Data availability: Ensure you have sufficient high-quality data to train and operate the AI agent effectively.
• Integration capabilities: Consider how well an AI agent can integrate with your existing systems and workflows.
• Cost-benefit analysis: Evaluate the potential return on investment, considering implementation costs and long-term benefits.
• Scalability needs: Assess if your business requires a solution that can easily scale to handle increasing demands.
• Human oversight: Determine the level of human supervision required and if your team can provide it.
• Ethical considerations: Reflect on any ethical implications of using AI agents in your specific industry or use case.
• Regulatory compliance: Ensure implementing an AI agent aligns with relevant industry regulations and data protection laws.
• User acceptance: Consider how your employees and customers will interact with and accept an AI agent solution.
• Long-term strategy: Align the implementation of AI agents with your company's long-term technological and business value.

Now that you have an idea of what factors affect your decision to use an AI agent, it’s time to think about how you might implement it. 

How to Make Your Own Autonomous Agents

Creating your own AI agent can be exciting and rewarding. Here’s the most efficient way to get started: 

Start with the End

To create autonomous agents, make sure to define your end goal. What specific problem are you trying to solve? What outcomes do you want to achieve?

Having a well-defined objective will guide your entire development process and help you measure success. For example, if your goal is to improve customer service, you might reduce response times, increase customer satisfaction scores, or handle a higher volume of inquiries without increasing staff.

Define Purpose and Scope

Once you've established your overall goal, it's time to define the specific purpose and scope of your AI agent. This involves outlining the exact tasks it will perform, the environment it will operate in, and any constraints or limitations it must adhere to.

For example, if you're creating a customer service AI agent, you might specify that it will handle initial customer inquiries, provide basic troubleshooting, and escalate complex issues to human agents. Clearly defining these parameters will help guide your development process and ensure that your AI agent meets your business needs effectively.

Prepare Your Team and Resources.

Creating an AI agent requires a skilled team and adequate resources. Assemble a team with expertise in AI development, data science, and domain knowledge relevant to your agent's purpose. 

Ensure you have the necessary computational resources, including powerful hardware or cloud computing capabilities, to train and run your AI model effectively. You should also factor in how much energy AI uses, so you can forecast and adequate budget.

Consider partnering with specialized AI companies like Arcee AI, which offers a flexible chat UI and workflow automation system that lets you create your own AI agents for complex tasks. Arcee AI's platform, Arcee Orchestra, breaks queries into tasks and routes each to the best-suited SLM, creating a symphony of specialized AI agents. The result? Better answers, streamlined multi-step processes, and significantly lower computational costs. Particularly for enterprise resource planning, Arcee AI offers tailored solutions that are both effective and secure, ensuring you get the best value for your AI deal.

Design the AI Agent.

When designing your AI agent, consider starting with an intuitive no-code approach. Companies like Arcee AI provide ready-to-use templates that let you create sophisticated AI workflows with just a few clicks. The no-code platform makes development accessible by allowing anyone to customize workflows based on their specific processes and data while maintaining enterprise-grade security through flexible deployment options.

Instead of building everything at once, focus on specific tasks that can help your department work better. You can create an AI agent that consistently delivers results by breaking down the process into smaller, well-defined steps.

As you plan your agent's decision-making process, you'll need to choose between basic and advanced approaches. Pick what makes sense for your situation.

Test and Validate.

Before deploying your AI agent, thoroughly testing and validating its performance is crucial. Create a comprehensive test suite that covers various scenarios and edge cases your agent might encounter. Evaluate its accuracy, response times, and effectiveness in achieving its goals. This testing phase lets you identify and address any issues or limitations before the agent goes live.

Ensure you also cover all ethical, security, and legal considerations, especially when dealing with customer data.

Deploy AI agents.

Once your AI agent is thoroughly tested and validated, it's time to deploy it in your production environment. Implement a robust monitoring system to track performance, detect anomalies, and gather user feedback. Regularly analyze this data to ensure your agent meets its objectives and identifies areas for improvement.

Keep improving

As with any system, it’s not a one-and-done deal, even if it’s a learning AI agent.

Keep improving your AI agent over time:

• Regularly check how well it's working
• Look for ways to make it better 
• Update its training data and how it thinks 
• Try out different versions to see which one works best 
• Use the version that gives the best results.

When you start building AI agents, it can seem overwhelming. The trick is to keep your end goal in mind and leverage an agentic AI-building platform from companies like Arcee AI.

Best Example of an AI Agent

You might think of futuristic robots that can do chores in your house as the best example of an AI agent. While that’s definitely where the future is going—thanks to innovations like the Tesla Bot—the reality is that you’re already using AI agents in your everyday life that are constantly working in the background.

The reality is that the best AI agents are often practical and can have an immediate impact on your daily life. Let’s take EarnUp's AI Advisor as an example. It helps individuals manage their debt and improve their financial health. Here are its key features:

• Smart Payment Optimization: EarnUp's AI agent automatically analyzes loan terms and payment schedules to find opportunities for interest savings.
• Intelligent Income Analysis: The system adapts payment schedules based on users' income patterns and financial obligations.
• Automated Payment Management: Handles payment processing and scheduling across multiple loans and financial accounts.
• Proactive Risk Detection: Identifies potential financial challenges before they become problems and suggests preventive measures.

What makes this AI agent particularly effective is it’s a complete solution. It provides advice, but it can also execute actions based on that advice.

AI Agent FAQs

What is an AI agent?

An AI agent is a system designed to perform tasks autonomously. It uses artificial intelligence to perceive its environment, make decisions, and take actions to achieve specific tasks. AI agents can learn, adapt, and improve their performance over time.

Is ChatGPT an AI agent?

ChatGPT is an AI language model, not a fully autonomous AI agent. While it can process language and generate responses, it cannot perceive its environment, make decisions, or take actions independently. It's a component that could be part of an AI agent system but not an agent itself.

Are AI Agents free?

The availability and cost of AI agents vary. While some basic AI tools are free, more advanced AI agents often require payment. Costs can range from subscription fees to usage-based pricing, depending on the complexity and capabilities of the AI agent(s).

Learn More about the future of AI Agents

AI agents are revolutionizing businesses with their ability to automate tasks, make decisions, and adapt to complex environments. From simple reflex agents to sophisticated learning systems, these intelligent tools offer tons of potential for innovation and efficiency across industries.

Consider your ultimate goal and resources when choosing to use an AI agent for your business. Ideally, start with something simple first while also thinking about how you'll scale.

Ready to explore how AI agents can transform your business? Book a call with the Arcee AI team today to discover how custom AI agents can address your unique use case and drive your organization forward.