That said, I\u2019m an optimist in every sense of the word and don\u2019t think we need to worry about our jobs or AI agents. This article is all about AI and AI agent usage. I\u2019ll explain what an AI agent is, how it works, the benefits, drawbacks, and types, and how to use AI agents in a way that actually works. This article leans into AI agents positively, finding solutions to potential drawbacks. It helped me see how many AI agents are already in my life, with no disastrous consequences.<\/p>\n
Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n An AI agent is software that can interact with its environment or the data input<\/strong>. The AI agent can process information and then provide solutions.<\/p>\n To help understand what an AI agent is, see the screenshot below. It demonstrates an AI agent, in the form of a chatbot, at work.<\/p>\n The chatbot has processed its environment and the data input (the question I asked), then responded with a relevant and helpful reply.<\/p>\n Later, I\u2019ll break down the types of AI agents, how they work, and what they can be used for.<\/p>\n <\/a> <\/p>\n Source<\/em><\/a><\/p>\n As demonstrated in the helpful infographic above, there are four main components in the AI agent:<\/p>\n The steps are:<\/p>\n AI agents are not something to be daunted by. In fact, many AI tools are used by both small businesses<\/a> and large, not to mention your personal life (I\u2019ll show you how later).<\/p>\n Let\u2019s understand how AI agents work using my HubSpot Chatbot<\/a> example above.<\/p>\n I gave the chatbot an input. I said: \u201cHello, where can I find pricing information?\u201d<\/p>\n The chatbot perceived and processed the information I had provided, even contextualizing the query and \u201cunderstanding\u201d this isn\u2019t a simple answer.<\/p>\n The output was a message and a question. The chatbot said, \u201cHi there \ud83d\udc4b Welcome to HubSpot Sales! You can find pricing information in the chatbot or on our website. To point you in the best direction, what specific package or product are you looking to get pricing for?\u201d<\/p>\n I liked the question back. It shows just how well the chatbot can perceive and process information; it knows it needs more information.<\/p>\n From this response, I knew how to get the pricing information I wanted, but the chatbot also encouraged further interaction so it could be more helpful. An example of an AI agent done well.<\/p>\n <\/a> <\/p>\n Source<\/em><\/a><\/p>\n According to my research, the chatbot is one of the most familiar types of AI agents. When I reached out to people asking about their uses of AI agents, 51% of responses were on chatbots and customer service support. Naturally, I\u2019ve included the benefits and drawbacks of those, but I wanted to dig deep and find other not-so-well-known benefits.<\/p>\n I\u2019ve pulled in insights from people using AI agents, and I was impressed with the openness of those who contributed and their willingness to acknowledge the pros and cons of AI agents. I\u2019ll share what I learned below.<\/p>\n Christian Hed<\/a>, CMO of Dstny<\/a>, has a lot of hands-on experience with AI agents. He was responsible for launching an AI chatbot agent at Dstny and deployed it with thousands of companies internationally.<\/p>\n AI agents can help customers handle thousands of customer support queries through simple interactions. We\u2019ve already reviewed an exchange with a chatbot above, which was fit for this purpose.<\/p>\n Hed explains, \u201cMany companies receive more than ten thousand customer support queries per day. Some receive direct calls that ping directly into an individual or team phone line, which can be a nightmare.<\/p>\n \u201cAI agents are perfect for handling those initial interactions, like \u2018Who do you want to talk to?\u2019 or \u2018What is this about?\u2019 which significantly reduces the number of unnecessary interactions between human agents and customers.<\/p>\n \u201cMany calls with easy responses can also get answered immediately by the AI agent \u2014 saving everyone time.<\/p>\n \u201cIf you have ten thousand inbound customer support tickets a day, this can save hundreds of hours of time over a week. This last year of AI innovation has cut costs dramatically for customer support teams.\u201d<\/p>\n What I like:<\/strong> I have to admit, I am a big fan of AI agents used in this way, as long as the process is efficient. I don\u2019t mind answering questions like \u2018What is this about?\u2019 or \u2018Who do you want to talk to?\u2019 if it speeds up my chances of reaching the correct department or person.<\/p>\n Aljay Ambos<\/a>, head of marketing and AI expert at Twixify<\/a>, has an excellent use case and benefits from an AI agent. He uses AI agents as strategic collaborators during campaign ideation. When Twixify is ready to release new features, the team analyzes different types of information, such as customer feedback, market trends, and what competitors are doing.<\/p>\n But, they\u2019re not doing this manually. Ambos uses Twixify\u2019s AI agent to speed up the process.<\/p>\n Ambos said, \u201cOur AI agent scanned thousands of customer support conversations. It presented a common problem: Many users needed assistance adjusting AI-made legal documents to comply with local rules. Such understanding, which a human team might not have seen because of the quantity of work, became the foundation idea of our campaign.\u201d<\/p>\n Based on the AI agent\u2019s findings, Twixify launched its campaign. I asked Ambos if there was a measurable success, and there was. Ambos said, \u201cOur strategy boosted user engagement by 36%. It also enabled more people to use the new feature we introduced.\u201d<\/p>\n What I like:<\/strong> Aside from the measurable success, I love this use of an AI agent \u2014 and I think it makes a lot of sense. AI agents can process data more efficiently than any human. I can see how the Twixify team would feed in large amounts of data from several sources to map out features they need to roll out to be competitive and meet customer needs.<\/p>\n Iryna Balaban is the CEO and co-founder of Elite Maids NY<\/a>, a cleaning service in New York City.<\/p>\n Before Balaban introduced an AI agent for inventory management, keeping track of cleaning products for thousands of cleaning jobs was a logistical disaster. Now, Balaban uses an AI agent and describes its mission to improve inventory management as a \u201ctremendous success.\u201d<\/p>\n Balaban describes how the AI agent tracks inventory: \u201cThe AI keeps track of cleaning supplies such as disinfectants, general-purpose cleaners, and microfiber cloths. [It] calculates usage history and anticipates upcoming cleaning. The AI knows when items are running out of stock and automatically reorders them. This way, our cleaning crews are never without supplies, which means there is never any delay or disruption in our service.\u201d<\/p>\n Impressive.<\/p>\n The quantitative measures are easy to spot: There are no delays for customers, and frustrations to staff are diminished as they turn up to work with all the tools they need to do their best job.<\/p>\n Curious, I asked Balaban if there was a measurable impact.<\/p>\n There was. \u201cThis AI integration has had profound effects. Out-of-stock cases have decreased by 90% since implementing the system. This not only increases our client satisfaction but saves us money. Automating reorders and avoiding emergency purchases at the last minute has saved us some money on cleaning products. Also, AI allows us to detect patterns in our usage data so that we can get bulk purchase discounts from vendors and further grow our profit margins.\u201d<\/p>\n What I like<\/strong>: This might be one of my favorite uses of an AI agent. It is an excellent example of AI automation<\/a> understanding its environment and the information supplied, and how an action (reordering stock) can take the mental load off people. I like how impactful this is. It is also an excellent example of how AI agents benefit people: repetitive, boring tasks, like reordering stock and managing inventory, are given to the AI so humans can do what they love.<\/p>\n Source<\/em><\/a><\/p>\n <\/a> <\/p>\n If you want to integrate AI agents into your business, it helps to know the drawbacks and common pitfalls so you can avoid them. Below, I\u2019ve listed drawbacks and some solutions to avoid them when you integrate your own AI agent.<\/p>\n Since chatbots were the most common use of AI agents in my research, let\u2019s start there.<\/p>\n Although Christian Hed has an innovative AI agent solution that reduces customer service burnout and increases the speed at which a customer can get an answer, he recognizes that some queries are beyond the AI agent. In these scenarios, the AI agent can cause frustration.<\/p>\n I appreciated Hed\u2019s openness to acknowledge the pros and cons of AI agents, particularly with this matter.<\/p>\n Hed said, \u201cSome queries are more complex and need a human to answer. An AI agent just causes frustration, as the customer knows they need support and wants to bypass it.\u201d<\/p>\n What I like: <\/strong>I agree entirely with this. In fact, it was the first sticking point that came to my mind when I considered the drawbacks of AI agents. This is a disadvantage that I\u2019m sure many of us can relate to. A personal recommendation for eliminating this issue is to use an AI agent that can quickly escalate a query to a human rep when the time calls for it. I feel most satisfied with AI agents that respond quickly to the request to speak with a human.<\/p>\n I spoke to 55 experts about AI agents, and eight people mentioned the idea of \u201cempathy.\u201d It was a fairly common theme amongst AI experts when it came to drawbacks.<\/p>\n Empathy is a drawback of the AI agent; it simply can\u2019t do it. If you have someone using an AI agent who really wants human connection, AI isn\u2019t going to cut it.<\/p>\n There are solutions to avoid this, though.<\/p>\n John Russo<\/a> is VP of healthcare technology solutions at OSP Labs<\/a>. Russo says, \u201cTo combat [lack of human empathy], businesses can implement AI agents in tandem with human agents. This hybrid approach ensures that more complex or emotionally sensitive queries are handled by real people, while routine tasks are automated. Additionally, constant fine-tuning and regular updates of the AI models can help reduce errors.\u201d<\/p>\n In the world of healthcare, emotionally sensitive queries are going to be common for Russo and OSP Labs.<\/p>\n Russo explains how OSP balances chatbots and humans, \u201cChatbots handle FAQs on our website, improving response times and allowing human agents to focus on more complicated issues.\u201d<\/p>\n What I like: <\/strong>As I said in my intro, I like being able to serve myself when it\u2019s efficient. Chatbots handle FAQs, response times, etc., while human agents handle complicated or sensitive queries. Perfection.<\/p>\n There is always an option not to use AI agents for particular purposes.<\/p>\n Parker Gilbert<\/a>, co-founder at Numeric<\/a>, recommends \u201c[using] AI agents strategically, focusing on tasks where human interaction isn\u2019t essential or incorporating human oversight where needed.\u201d<\/p>\n What I like:<\/strong> A strategic analysis of where an AI agent best works<\/a> is beneficial (or where it isn\u2019t) seems like the minimum a company can do for its customers. Considering where AI agents are most valuable will give you all the benefits of AI while avoiding the drawbacks.<\/p>\n I think there\u2019s a temptation to be too confident in AI agents to do their thing. But as Kevin Shahnazari<\/a>, founder and CEO of FinlyWealth<\/a>, learned, it doesn\u2019t always provide accurate and helpful actions.<\/p>\n FinlyWealth uses AI agents across its credit card recommendation platform.<\/p>\n Shahnazari explains how the AI agent works, \u201cThe AI agent analyzes credit card application patterns. Our agents process thousands of data points to predict approval odds, saving users from damaging their credit scores with failed applications.\u201d<\/p>\n Shahnazari credits the system with \u201c[preventing] over 2,000 likely rejections while identifying better-matched card options for users.\u201d<\/p>\n The challenge that Shahnazari found was that their AI agents started making overly conservative recommendations.<\/p>\n When asked how to solve this, Shahnazari said, \u201cWe solved this by implementing a hybrid approach where agents flag potential matches for human review. This combination improved recommendation accuracy by 35% while maintaining the efficiency of automated screening.\u201d<\/p>\n What I like:<\/strong> While reliable, unless maintained and trained properly on good data sources, the AI agent\u2019s quality may be low, or it could deteriorate. The value in what Shahnazari said is in the AI agent hybrid approach, adding that layer of human review. It\u2019s a nice crossover with the hybrid approach mentioned earlier regarding AI and empathy.<\/p>\n I think the nice thing about a hybrid approach and making hybridity known to your workforce is that you will give them assurance that AI is not going to take their job<\/a> anytime soon. Ultimately, AI agents and humans are better at working together. I also like that the AI agents can flag humans when action is needed, saving humans from having to constantly watch the AI.<\/p>\n <\/a> <\/p>\n I think it helps to break down the types of AI agents to increase understanding and, therefore, remove the fear. Knowledge is power, and I think there\u2019s a good chance you\u2019ll be surprised by the types of AI agents that you are using nonchalantly and without threat in your day-to-day life. Even with an optimistic viewpoint, this was a pleasant surprise for me.<\/p>\n Learning agents are the most familiar type of AI agent. You will have experienced learning agents if you shop online, stream TV, or listen to music online.<\/p>\n Learning AI agents work by improving their performance over time by learning from experience. For example, if you watched three movies over three days with the same actor and watched those movies to the end credits, then a learning agent might suggest another movie with the same actor.<\/p>\n Best for:<\/strong> AI-powered personal assistants, fraud detection systems, and self-learning robots.<\/p>\n You\u2019ll be familiar with this type of AI agent if you\u2019re into smart homes. You might find this use case particularly non-threatening.<\/p>\n Simple reflex agents can be used in things like thermostats. These agents don\u2019t have any context (unlike model-based, coming next). Simple reflex agents act based on the current situation without considering past experiences. They follow condition\u2013action rules (if X happens, do Y). So, sticking with thermostats: If the temperature drops below a certain temperature, the heating kicks on. If the desired temperature is met, it turns off.<\/p>\n Best for:<\/strong> Basic automated systems like thermostat controls, spam filters, or rule-based chatbots.<\/p>\n Model-based reflex agents are a type of AI agent that maintains an internal model of the world. They make decisions based on both current input and, importantly, stored knowledge. This helps the AI make informed decisions that are aligned with your brand.<\/p>\n Think of your model-based reflex agent as an AI with a boundary. It requires context assistance so that it understands and responds in accordance with your brand values, for example.<\/p>\n With these agents, you can input data that helps keep the AI on track, ensuring it makes decisions within a pre-defined framework.<\/p>\n Simply, these could be generative AI models that take into account your tone of voice.<\/p>\n Best for: <\/strong>AI assistants, like HubSpot\u2019s Breeze Copilot<\/a>.<\/p>\n Check out HubSpot\u2019s AI Breeze Copilot<\/a>.<\/p>\n Goal-based agents are a type of AI agent that you might be already using. Is your sales team using a CRM with built-in AI<\/a>? You\u2019re likely already moving and grooving with goal-based agents.<\/p>\n Goal-based agents make decisions based on pre-defined goals. The agent can evaluate the environment and select an action that brings a pre-defined goal closer.<\/p>\n Sticking with the CRM example, goal-based agents can assist sales teams in knowing what actions to take for which prospects. Instead of sales personnel following arbitrary actions for every prospect, a goal-based agent might spot commonalities between prospects that close and identify the information or action they want from your sales teams.<\/p>\n The goal-based agent, built within a CRM, can suggest sales strategies based on data. It might tell you to send follow-up emails because a follow-up email after xx days has xx% success rate. It takes the overwhelm out of what to do next and provides data-driven, goal-orientated action. Not bad, eh?<\/p>\n Best for: <\/strong>CRMs<\/p>\n Get started with HubSpot CRM<\/a>.<\/p>\n <\/a> <\/p>\n AI agents generally don\u2019t work perfectly immediately, but they are pretty good. If you\u2019re taking AI agents seriously, then you may benefit from these tips that will help you use AI agents that actually work.<\/p>\n I introduced Aljay Ambos earlier; he used the AI agent to come up with new features. Since his use case was so impactful, with a 36% boost in user engagement, I asked him for tips on how he got there.<\/p>\n Ambos said, \u201cWe trained it. We made special datasets that focus on the specific language, worries, and goals of our audience.<\/p>\n \u201cIf you are considering using AI agents, focus on making them personal. A standard AI model can only help you to a certain point. When you customize it to understand your niche and integrate seamlessly with your team, you can go beyond simple automation and build a real partnership that produces significant results.\u201d<\/p>\n This point of using AI strategically has cropped up earlier in this article, but I wanted to keep it here as I think it is essential if you want to use AI agents in a way that works.<\/p>\n The world is abuzz with AI, its benefits, and all the use cases. It is very easy to get distracted by the promise of an AI agent that will change your life or business, but if you\u2019re not integrating AI strategically, it will fail.<\/p>\n Matthew Franzyshen<\/a>, business development manager at Ascendant Technologies Inc.<\/a>, warns that businesses must integrate AI agents into their workflows. He says, \u201cMake sure that you actually know what you need them for. Don\u2019t add AI agents just because that\u2019s what everyone else is doing. Audit and understand your business needs so you can choose one that is aligned with your unique business goals.\u201d<\/p>\n I like Franzyshen\u2019s tip. If you audit your business and understand why<\/em> you\u2019re integrating an AI agent, you\u2019ll be more likely to use it (especially if it\u2019s integrated into workflows) and assure staff who feel AI is going to replace them.<\/p>\n There are ways in which you can manage chatbot outputs and customer satisfaction using AI agents.<\/p>\n Nikita Sherbina<\/a>, co-founder and CEO at AIScreen,<\/a> provides an example of how managing chatbots contributes to success. He says, \u201cA specific example of our success is the implementation of AI chatbots, which handle 80% of routine customer queries, reducing response times by 60%. To maximize effectiveness, we continuously refine chatbot scripts based on user feedback and ensure seamless escalation to human agents for complex issues.\u201d<\/p>\n I like this idea. No matter how much information you give an AI agent, you don\u2019t know how effective it will be until you try it. No doubt, once you put a chatbot in front of your customers, you\u2019ll get feedback, good and bad, about how it works. It\u2019s important to consider all feedback and work on improving the chatbot bit by bit.<\/p>\n The good news is an AI agent can help you analyze all the feedback, just like Ambos from Twixify taught us earlier.<\/p>\n![\"Download](\"https:\/\/no-cache.hubspot.com\/cta\/default\/53\/b72f2b25-8cc9-4642-9a1b-1e675d3d273b.png\")
<\/a><\/p>\n\n
What is an AI agent?<\/h2>\n
![\"screenshot](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-2-20250317-6062074.webp\" "\\"\\"")
<\/p>\nHow do AI agents work?<\/h2>\n
![\"infographic](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-3-20250317-9750827.webp\" "\\"\\"")
<\/p>\n\n
\n
Benefits of AI Agents<\/h2>\n
![\"infographic](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-4-20250317-1399173.webp\" "\\"\\"")
<\/p>\nReduce customer service burnout and increase customer satisfaction.<\/h3>\n
Analyze large data sets to improve products based on customer feedback.<\/h3>\n
Inventory Management<\/h3>\n
![\"infographic](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-5-20250317-2313048.webp\" "\\"\\"")
<\/p>\nDrawbacks of AI Agents<\/h2>\n
Complex queries need a human.<\/h3>\n
Lack of Empathy<\/h3>\n
Take a hybrid approach.<\/h4>\n
Avoid AI agents entirely when human connection is needed.<\/h4>\n
Managing Data Accuracy<\/h3>\n
Types of AI Agents<\/h2>\n
1. Learning Agents<\/strong><\/h3>\n
2. Simple Reflex Agents<\/strong><\/h3>\n
3. <\/strong>Model-Based Reflex Agents<\/strong><\/h3>\n
![\"screenshot](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-6-20250317-7329788.webp\" "\\"\\"")
<\/p>\n4. Goal-Based Agents<\/strong><\/h3>\n
![\"screenshot](\"http:\/\/www.backstagelenses.com\/wp-content\/uploads\/2025\/03\/ai-agent-7-20250317-7797678.webp\" "\\"\\"")
<\/p>\nHow to Use AI Agents That Work<\/h2>\n
Train them.<\/h3>\n
Be strategic.<\/h3>\n
Refine chatbot scripts.<\/h3>\n
![\"\"](\"https:\/\/track.hubspot.com\/__ptq.gif?a=53&k=14&r=https%3A%2F%2Fblog.hubspot.com%2Fsales%2Fai-agent&bu=https%253A%252F%252Fblog.hubspot.com%252Fsales&bvt=rss\")
<\/p>\n","protected":false},"excerpt":{"rendered":"