In the ever-evolving tech landscape, AI agents will soon be indispensable tools for businesses, seamlessly augmenting human professional and technical teams. Moreover, these advanced systems are not just about automating mundane tasks; they are transforming the way we analyze data, make decisions, and manage our daily operations. As businesses increasingly rely on AI to stay ahead, understanding the unique capabilities of an AI agent is more critical than ever. Indeed, the key to unlocking their full potential lies in knowing how to integrate them effectively and leverage their strengths to gain a competitive edge.

In this article, I’ll look at the capabilities of AI agents. In particular, I’ll tell you how they are different from AI assistants, chatbots, and traditional bots. Also, I’ll identify for you their key components and provide real-world examples of how they can transform your business. Moreover, I’ll identify the best ways for business to select a pre-built AI agent from a tech vendor or to build them in-house. Interestingly, the process of choosing an AI agent is starting to mirror the hiring process we go through for human business professionals and specialists. 

1. What Is an AI Agent and How It Is Different From ChatBots, AI Assistants, and Traditional Bots.

Let’s start with a definition for AI agents. See below:

“An AI agent is a software program that can interact with its surroundings, gather information, and use that information to complete tasks on its own to achieve goals set by humans.”

Geeks For Geeks

So, an AI Agent has a lot more capabilities and “intelligence” than a plain-old AI Chatbot or traditional bots. The key differences include:

The AI Agent Difference

• Autonomous. Today’s AI Agents have a high degree of autonomy because they are driven by goals to take independent action. On the other hand, AI Assistants, ChatBots and Bots require more user input or pre-programmed rules on how to perform simple tasks effectively.
• Handles Complexity. Here, AI Agents can operate in more dynamic environments to achieve their assigned goals. In comparison, AI Assistants, ChatBots and Bots operate in pre-defined environments and can perform only simple interactions. 
• Learns and Adapts. Lastly, AI agents use machine learning (ML) to adapt and improve their performance over time. In contrast, AI Assistants and Bots typically do not have “learning” abilities. Hence, to change their behavior requires software updates.

Also to be clear, AI chatbots are increasingly more agentic from the “classic” generative AI chatbots such as the 2023 ChatGPT. For one, these agentic chatbots are now connected to external tools such as “live” knowledge bases. Also, they are providing increased accuracy using agentic capabilities such as retrieval augmented generation (RAG). So, more and more we are interacting with agentic chatbots versus the traditional rules-based chatbots and standalone, first-generation AI chatbots.

2. AI Agent Components: Plans, Perceives, Analyzes, Decides, Acts, Learns

Also, AI agents are built with a robust framework that includes several key components. These capabilities include planning, perceiving, analyzing, deciding, acting, and learning. What’s more, these AI components act in concert based on an overall goal or input. Moreover, these AI agents are increasingly getting smarter, making less mistakes. Below are the key AI agent components and examples how they work.

AI Agent Components

a. Plan: Based on Goals, It Plans, Initiates, and Controls Other AI Agent Components. 

For example, an AI agent planning component is tasked to develop a conference agenda for the CEO to kickoff a new company initiative. So, the AI agent develops a plan that includes a schedule for a series of meetings. Moreover, details of the plan lists all key stakeholders and a detailed agenda to convey the company’s new initiative. Most importantly, the AI agent’s planning component does not work in isolation when it is given direction on what its role and purpose is. Once this planning component develops its overall plan, it initiates and controls other AI agent components to achieve the overall purpose and planning tasks.

b. Perceive: Gathers Information and Observes What Is Happening.

To illustrate this perceive component, the AI agent monitors market trends and competitor activities in real-time, alerting the marketing team to potential opportunities or threats. Here, the AI agents can use application programming interfaces (API) and Internet of Things (IoT) sensors to collect data about its situation. This input can include images, sounds, and textual data to name a few. Also, it can use descriptive analytics to further summarize and clarify what it is “seeing”.

How Data And AI Work Together To Better Empower Analytics

c. Analyze: AI Agents Use the Full Range of Analytics to Orient and Deliver Answers and Recommendations.

For instance, the AI agent reviews sales data and customer feedback to identify which products are underperforming and suggests targeted marketing strategies to boost their performance. Again, an AI Agent is designed to work with its other components and determines which type of analytics to use to best fulfill its goals. This could include the full range of analytics to include descriptive, diagnostics, predictive, or prescriptive that ultimately leads to the best decision, and ultimately, the desired action.

d. Decide: AI Agents Go Beyond Making Recommendations.

For example, the AI agent makes a decision by selecting the most cost-effective supplier for raw materials based on a comprehensive analysis of price, quality, and delivery times. So, an AI agent goes much further than just providing answers and recommendations. Based on its overall mission and constraints, it can make decisions. Subsequently, the AI agent’s decisions are then translated into action, either by itself or implemented by other automation.

e. Act: Executes Its Decisions Based on Its Previously Provided Directive.

To illustrate, the AI agent automates the process of generating and sending out monthly financial reports to all department heads, ensuring timely and accurate distribution. Here, the action module translates decisions into real-world actions. In these cases, the AI Agent uses actuators to act in the real-world. This can include robotic arms, wheels, or a software tool to name a few. Moreover, it implements its actions seamlessly based on the decisions it made previously and within its overall assigned goals.

f. Learn: Receives Continuous Feedback and Adapts to Better Meet Its Overall Purpose.

As an example, the AI agent refines its predictive models for customer churn by continuously analyzing customer interactions and feedback. As a result, this leads to more effective retention strategies. So, this learning component of the AI Agent has a memory that stores and retrieves past experiences. As a result, it learns from these experiences and makes adjustments to itself to improve its future results.

3. Five Types of AI Agents: Ranging from Simple Rule-Based to Advanced Learning Systems

AI agents come in various forms, each with its own level of complexity and capabilities. Moreover, AI Agents can work in concert with other AI Agents and automation.  Below, I describe five different types of AI agents as well as how a multi-agent AI system works.

Types of AI Agents

a. Simple Reflex Agents: Acts on Condition-Action Rules.

For instance, a simple reflex agent automatically sends a thank-you email to customers immediately after they make a purchase, enhancing customer service with a straightforward, rule-based action. These are the most basic type of AI agent that follow predefined rules and make decisions without consideration of past experiences or future consequences.

b. Model-Based Reflex Agent: Compares the Current Environment to an Internal Model.

As an example, a model-based reflex agent uses historical data to predict inventory needs and automatically places orders when stock levels fall below a certain threshold, ensuring that the warehouse is always well-stocked. These agents are more advanced than simple reflex agents. In this case, the agent has an internal model of the “world”. Hence, it can track the current state of the environment and compare it to its internal model to make better, informed decisions.

c. Goal-Based Agent: Acts Autonomously Guided by Its Overall Goal.

To illustrate, a goal-based agent helps a project manager by identifying the most efficient sequence of tasks to complete a project on time, taking into account resource availability and deadlines. Here, goal-based agents do not just react to environmental stimuli. Specifically, these AI Agents incorporate a proactive, goal-oriented approach to problem-solving. Further, they can evaluate different possible actions and select the one that best meets its overall goals.

d. Utility-Based Agent: Factors in Utility Value in Its Decision-Making.

For example, a utility-based agent evaluates multiple investment options by considering factors like risk, return, and market conditions, and recommends the portfolio that maximizes the company’s financial utility. Moreover, if enabled, the AI agent can execute stock trades. Here, the utility-based reflex agent not only focuses on simple goal achievement, but uses a utility function to evaluate and select actions that maximize overall benefit. More specifically, this type of AI agent assigns a utility value to each possible outcome. Especially where there are multiple goals or tradeoffs, the utility-based agent can make better, consistent decisions.

e. Learning Agents: Adapts Over Time to New Experience and Information.

For instance, a learning agent continuously analyzes customer support interactions to improve its responses, reducing resolution times and enhancing customer satisfaction over time. So, a learning agent improves its performance over time by adapting to new experiences and data input. These types of agents are best in dynamic or uncertain situations. 

f. Multi-agent Systems: AI Agents Working in Concert.

In this case, a multi-agent system can coordinate the efforts of different departments, such as sales, marketing, and customer service, to optimize lead generation and conversion processes, ensuring a seamless and efficient workflow. Here, an overall AI agent can orchestrate multiple agents to achieve its goals. Hierarchically, these agents handle complex problems by breaking down tasks into smaller, manageable subtasks. The higher-level agents focus on goals, while the lower-level agents handle more specific tasks.

For more information on types of AI agents, see IBM’s article, Types of AI agents and Writesonic’s articles, 7 Types of AI Agents to Streamline Your Workflow.

4. Selecting Your Next AI Agent Specialist: Key Skills and Expertise to Look for in an AI Job Candidate.

Nowadays, selecting your next AI agent specialist is much like evaluating a human job candidate. For instance, look for tech vendors’ AI agent candidates that have a strong background in machine learning, data analysis, and software engineering, along with having a deep understanding of your business domain. Also, key AI agent skills include problem-solving, critical thinking, and the ability to communicate complex technical concepts to non-technical stakeholders. 

Remarkably as AI agents and chatbots mature, they are becoming more professional and reliable. Indeed, they are far surpassing the ChatGPT of 2023 with its “smart summer intern” chat capabilities. To illustrate, below are sample job descriptions of today’s AI agents. Also, I’ll highlight tech vendors growing capabilities that enable businesses to build AI agents in-house.

a. Google Clout AI Agent “Job” Descriptions and Experience Required.

Now, AI agents are starting to have domain expertise such as marketing, finance, and supply chain to name a few. Also, tech vendors are starting to describe their pre-built AI agents much like a business would describe a job opening for a human job candidate. For example, below is how Google Cloud is categorizing AI Agents.

• Customer agents deliver personalized customer experiences by understanding customer needs, answering questions, resolving customer issues, or recommending the right products and services. They work seamlessly across multiple channels including the web, mobile, or point of sale, and can be integrated into product experiences with voice or video.
• Employee agents boost productivity by streamlining processes, managing repetitive tasks, answering employee questions, as well as editing and translating critical content and communications. 
• Creative agents supercharge the design and creative process by generating content, images, and ideas, assisting with design, writing, personalization, and campaigns. 
• Data agents are built for complex data analysis. They have the potential to find and act on meaningful insights from data, all while ensuring the factual integrity of their results. 
• Code agents accelerate software development with AI-enabled code generation and coding assistance, and to ramp up on new languages and code bases. Many organizations are seeing significant gains in productivity, leading to faster deployment and cleaner, clearer code. 
• Security agents strengthen security posture by mitigating attacks or increasing the speed of investigations. They can oversee security across various surfaces and stages of the security life cycle: prevention, detection, and response. 

Indeed, it is a bit unnerving in the way tech vendors are describing their AI Agent’s capabilities much like a job description for a business professional or specialist.

b. DIY AI Agents and Orchestrating Pre-Built AI Agents for Your Workplace.

Moreover, vendors are beginning to offer Do-It-Yourself (DIY) AI agent kits that allow organizations to customize and build their own AI agent solutions tailored to their specific needs and business domains. For example, a company might develop a simple chatbot to handle common customer inquiries, using pre-defined rules and scripts. Moreover, vendors, such as IBM, are offering AI agents solutions that allow businesses to orchestrate pre-built AI agents by integrating them together and customizing them to operate within an existing workplace. 

More References.

Here Are 9 Examples Of Artificial Intelligence (AI) Technology That Will Best Empower Supply Chains

Below are more references on these AI agents that are starting to be indispensable tools for businesses.

• Nabih Sabeh’s article, Chatbots vs AI Agents: Understanding the Difference in Simple Terms
• Google Cloud’s article, What is an AI agent?
• Zapier’s article, What are AI agents? How they work and how to use them
• GeeksForGeeks’ article, Agents in AI
• botpress’ article, 36 Real-World Examples of AI Agents
• BCG’s article, AI Agents
• SC Tech Insights’ article, Here Are 9 Examples Of Artificial Intelligence (AI) Technology That Will Best Empower Supply Chains

Need help with an innovative supply chain solution that leverages emerging information technologies? I’m Randy McClure, and I’ve spent many years helping logistics organizations to make the most of new information technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in new strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your supply chain or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on AI, Data Analytics, and Decision Science.