Generative AI has become an increasingly integral part of day-to-day human life, and by extension, small businesses. Recently, we wrote an article covering different types of generative AI, those being:
- AI chatbots
- AI assistants
- AI agents
During this process, we made a few fascinating discoveries.
- Between all three kinds of generative AI, AI agents have the most autonomy. AI agents are forms of generative AI that are, as stated in our article, “primarily designed to pursue a given goal over time. Its primary function is the pursuit of said given goal, through a loop of planning, acting, observing results, and then adjusting. In technical terms, an AI agent is a type of intelligent agent, an autonomous AI system capable of perceiving its environment, making decisions, and taking actions to achieve specific objectives, and can optimize processes such as logistics and decision-making based on performance metrics.”
- AI agents are at the forefront of the generative AI discussion, to the point that about half of the article is just about agents!
- The potential for efficiency is not only huge in general with AI, but it’s at its hugest with AI agents. There’s potential for entire companies to be run just by AI agents in the future! AI agents can enhance productivity by automating repetitive tasks, allowing humans to focus on more creative work.
- The ethical concerns however are just as large, along with the potential risks that come with how much power AI agents can wield. AI agents can also struggle with tasks requiring deep empathy or complex human interaction, such as therapy or conflict resolution.
AI agents offer significant benefits, such as increased productivity and automation of repetitive tasks, but also present challenges including limitations in emotional intelligence, ethical decision-making, adaptation in unpredictable environments, and resource requirements. Despite this, AI agents can help small businesses reduce operational costs and boost productivity by saving up to 12 hours monthly. In fact, SMBs using AI agents report an average of 40% efficiency gains within the first year, with individual employees saving roughly 10–15 hours per week. Implementing AI agents allows small businesses to automate high-volume, low-complexity tasks, effectively creating a digital workforce that operates 24/7.
AI agents are built on agent technology, which refers to the underlying architecture and frameworks that enable autonomous systems to reason, make decisions, and operate within various domains. Agent technology underpins the functionality, decision-making, and organizational frameworks of AI agents and autonomous systems.
One of the main conclusions we came to in the article we wrote is that AI is used at its best when put towards menial tasks, particularly ones that are low stakes, and often require the processing of large amounts of repetitive information. Data entry/analysis, identification of potential/ongoing security threats, double checking human work, automation of routine tasks, and other such objectives are areas where AI agents are particularly strong. Developing and deploying sophisticated AI agents can be resource-intensive, making them unsuitable for smaller organizations. However, building AI agents is a strategic activity that enhances productivity, scales operations, and requires proper supervision and responsible use. AI agents can process multimodal information like text, voice, video, audio, and code simultaneously. So of course, we wondered, “If AI agents are so important, and so useful for repetitive and menial tasks, how can small businesses best implement them into their workflows?” Today, we’ll answer that very question in as much detail as possible. Welcome, to The Small Business Guide to AI Agents for Repetitive/Menial Tasks.
What is an AI Agent?
AI agents are intelligent software systems powered by artificial intelligence, designed to pursue specific goals and complete tasks on behalf of users or organizations. Unlike traditional automation tools, AI agents possess a high degree of autonomy. They can reason, plan, and remember past actions, allowing them to adapt and make decisions independently. Thanks to the multimodal capabilities of modern generative AI and foundation models, AI agents can process and understand a wide range of information types, including text, voice, video, audio, and even code, all at once.
This versatility enables AI agents to converse naturally, reason through problems, learn from experience, and make decisions that drive business processes forward. They can facilitate transactions, automate routine and complex workflows, and even collaborate with other agents to tackle more complex workflows that go beyond the abilities of a single agent. By orchestrating multiple agents, businesses can achieve seamless automation across various functions, ensuring that tasks are completed efficiently and accurately, even as business needs evolve.
How AI Agents Work
AI agents function by continuously observing their environment, whether that’s your business systems, customer interactions, or external data sources, to gather the information they need. Using advanced large language models (LLMs) and natural language processing, these agents can interpret user requests, analyze data, and plan their next steps. The core of their operation is an observe-plan-act cycle: they observe what’s happening, plan their actions based on available information, and then act to achieve their objectives.
As AI agents interact with your business operations, they learn from past interactions, identifying patterns and refining their strategies to become more effective over time. This self-improving loop allows them to perform complex tasks with increasing accuracy and efficiency, often with minimal human oversight. By leveraging a suite of AI tools and connecting to both internal and external systems, AI agents can automate repetitive tasks, streamline business processes, and respond to natural language instructions, making them invaluable for businesses looking to boost productivity and reduce manual workload.
Types of AI Agents
Custom AI agents are purpose-built solutions tailored to address the unique needs and challenges of your business. Unlike generic, off-the-shelf AI tools, custom AI agents are developed using a variety of AI models and machine learning techniques to perform tasks that are specific to your workflows, whether that’s automating customer data processing, managing specialized business operations, or supporting complex decision-making. These agents can be seamlessly integrated with your existing external tools and systems, allowing them to access and process large volumes of data, including sensitive customer information. By implementing custom AI agents, businesses can automate complex workflows, reduce the need for human intervention, and ensure that tasks are performed according to predefined rules and business logic. Additionally, custom agents can be designed to work alongside other AI agents in a multi-agent system, enabling coordinated decision making and the ability to handle even more complex tasks. Deploying agents enables organizations to address different operational needs and tasks across various business categories. This approach empowers organizations to deploy AI solutions that are not only efficient but also highly adaptable to changing business requirements.
For businesses with unique needs or specialized workflows, custom AI agents provide a powerful solution. Unlike generic, off-the-shelf options, custom AI agents are designed from the ground up to automate complex tasks that are specific to your organization. By leveraging advanced AI models, such as large language models (LLMs), and building multi-agent systems, these agents can tackle challenges that would be difficult or time-consuming for human teams to handle manually.
Autonomous agents on the other hand are particularly valuable for businesses that need to automate complex tasks that require a high degree of flexibility and responsiveness. They can navigate intricate systems, make decisions based on live data, and interact directly with human users, all while minimizing the need for human oversight.
Autonomous agents represent the next level of AI-driven automation, capable of operating independently and making decisions without the need for human oversight. These advanced AI agents use sophisticated models, such as model-based reflex agents and utility-based agents, to analyze their environment, weigh options, and perform tasks that require a high degree of autonomy.
In practice, autonomous agents can be deployed to automate complex workflows across business operations, from data analysis and processing to robotic process automation and conversational agents. By integrating with other AI systems and tools, autonomous agents can handle end-to-end processes, reducing the need for human intervention and allowing staff to focus on higher-value activities.
Advisory and Strategy Foundations
AI Readiness Assessment
Before we can begin the process of implementation, let’s focus on our foundations, your “why”s and “what”s before your “how”s, if you will. Let’s start with the former. Why are you looking to implement an AI agent into your system? Chances are, you want to increase efficiency, or save on labor, or something similar. However, it’s worth giving a friendly reminder that as of right now, a competent human being is more capable of discernment and completing tasks in a detail-oriented manner than any AI. This brings us into the “what” question. What exactly should you use an AI agent on?
Start by putting together a list of tasks your team repeats on a regular basis. Copying info into your CRM, appointment scheduling, invoicing and intake, reporting and status updates, and so on. The best task for an agent is one that is frequent, predictable, and low-risk, so take the time to identify these, and make sure these are the first things you automate. For AI agents to perform optimally, it is crucial that these are well defined tasks, clearly outlined and structured, so the agent can solve problems effectively and improve iteratively. When you do begin automation, start slowly. If you have doubts on whether an AI agent should be trusted with a task, do not use the agent (for now). While keeping with the times and using this innovative technology is a massive boon to your business, risk management is always also a concern.
Is your data usable for the agent? Keeping as much of the data being accessed by an agent as centralized and organized as possible will make using it much more efficient, and risk free. CRMs, FAQs, spreadsheets, things like this are the tools that help your AI agent do what has been asked of it. Think of it the same way you think of giving an AI chatbot or assistant a prompt. The more specific it is, the better.
For more risky tasks, consider using a human/AI agent duo. Refunds, cancellations, pricing/quotes, and so on are tasks no AI agent should be entrusted with on their own, but if there’s an approval system where a human can verify whether a response from the agent makes sense, this can make risk drastically decrease.
The ROI Question
Is this technology even worth the effort? We’ll be getting more into detail about just what kinds of ROI you can expect later in this article, but for now, I want to give you a fascinating anecdote.
We’ve recently begun the process of developing our very own AI agents here at GTC. It’s a slow process, especially since we are custom building them rather than buying one that’s ready out of the box (we’ll get into the pros and cons of this choice later too). An agent we’ve been developing is for performing a SWOT analysis, where we look at the strengths, weaknesses, opportunities, and threats facing a client. The process of a single SWOT analysis can take anywhere from 40 minutes to 2 entire hours for an experienced human being. Our agent? We currently are estimating it would be able to generate an analysis, post-prompt, in just 2 minutes, for a mere 4 cents worth of credits (this is a high ball by the way, and we would not be surprised if we can get analyses done for faster/cheaper). Even if we assume a SWOT would get done as quickly as humanly possible, a 40 minute task being completed in 2 minutes equates to a 2000% increase in time efficiency for just a few pennies.
Agent Governance Plan
Some agents are more autonomous than others. For most small businesses, a completely autonomous AI agent is simply too risky to implement. It’s almost always a good idea to make sure an AI has as many guardrails as possible. Checks and balances are important. As stated previously, assign AI agents to low risk, repetitive tasks, and consider a human approval process to help mitigate risk if AI agents are used for higher risk objectives. Even then, don’t get too bold. Use your agents wisely.
Use-Case Solutions: Examples of AI Agents at Work
Customer Support
AI agents, on a basic level, can provide support for your customers. Conversational agents, in particular, are designed to handle customer inquiries and provide natural language support, making interactions feel more intuitive and human-like. Using your policies and knowledge base, they can answer frequently asked questions, or explain certain policies.
AI agents can also collaborate with human agents to achieve shared goals, ensuring seamless customer support and effective issue resolution.
Make sure your agent is pulling primarily from a specific knowledge base, rather than the general internet, to avoid inaccuracies. Be sure to apply agents here carefully. An agent should be able to recognize when to pass a customer on to live support, and being able to do this quickly mitigates the risk of upsetting said customer. Even then, if a customer actually has a question and isn’t able to reach you immediately because of an agent standing in the way, that in itself can damage trust. Think of using an AI agent the same way you’d think of using an answering machine on your phone number. It should only be used if the benefits of robotic assistance outweigh the cost of a consumer’s trust and time. Assess your ROI accordingly.
Sales and Lead Follow-Up
Many small businesses lose out on clients simply because they can’t respond quick enough. Here, with the right monitoring, AI agents can pick up the slack. They can send an instant, personalized reply when a lead sends an email or fills out a form, ask clarifying questions, update your CRM, create reminders and follow-up tasks, and even draft proposals/quotes. Agents can also check if a lead is qualified for your company by analyzing their budget range, service area, timeline, problem type, or whatever other prerequisites. When done properly, agents can decrease response time, increase appointment set rates, turbocharge conversion rates, and slash no shows.
Operations Automation with Multiple AI Agents
This is one of the best places to implement AI agents. The work they do here is quiet, saves loads of time, and is also often low risk. Simple reflex agents, which operate on basic, rule-based logic, are especially effective for automating repetitive operational tasks that require straightforward stimulus-response actions. Large data sets can be moved in moments (think moving intake forms into spreadsheets), invoice info can be extracted and routed to the right place, and even flag anomalies (an AI agent is almost as useful for catching human errors as a human is for catching AI errors). If a decision affects money, contracts, customer promises, the deletion of important documents (even duplicates), or similar, have an approval process in place. Using this properly can save you entire hours of work every week.
Admin and Scheduling
This is where we want to start being really careful. When we say admin, we don’t mean admin work. Agents should be used as assistants, with the only difference between them and AI assistants being the ability to actually execute on simple menial tasks. Use agents for proposing times and scheduling meetings, sending reminders and rescheduling if needed, running onboarding checklists, drafting internal documents like SOPs and templates, etc. Think of an agent here like a stereotypical secretary.
Implementation and Integration of AI Agents
Workflow Design and Specificity
When working with an AI agent, being able to be specific with instructions helps greatly with mitigating risk, and ensuring you get the response you want to a prompt. Breaking a workflow down into steps helps an agent follow directions. For example, instead of saying “handle support emails”, give a series of steps:
- Classify the email
- Find the related KB article (FAQ, how-tos, troubleshooting guides, etc.)
- Draft a response
- Ask for approval if refund-related
- Log in ticket system
Any prompt you give an AI agent is just a set of instructions. If you don’t break things down, why would you expect a quality response? Be specific about tone, what information to use, what to avoid, and so on. Inform the agent of company policies as well. For example, you could tell an agent to never guess pricing, never promise refunds, and always cite a knowledge base when answering questions.
Have a trigger system in place as well. AI agents are in this case quite similar to automations, so ensuring the trigger is properly constructed can be the difference between an agent that wrecks everything, does nothing, or does exactly what you need.
Systems Integration
AI agents are at their best when they have access to the right information and systems. AI systems can integrate with business infrastructure to enable seamless data access and workflow automation, allowing agents to interact directly with core tools and databases. Of course, privacy is a concern here. All data an agent uses is data that may then be used by that agent’s LLM for future responses, so make sure you make your clients aware of privacy concerns. However, with proper access to systems like the CRM, help desk, company email/calendar, spreadsheets, and APIs/webhooks, agents will be able to perform work with more accuracy and efficiency.
How To Actually GET An Agent: Build vs Buy
At this point, you should have a solid plan put together on how and where to implement an AI agent. Now for the big question: “How do I get an AI agent?” You have two options here. You can either completely build one from scratch, or you can “buy” one.
The easiest and most obvious option at first would seem to be to simply buy an AI agent. Many cloud-based platforms now offer pre-built AI agents, such as Google Cloud’s solutions, agent frameworks, and marketplaces, which allow small businesses to quickly deploy ready-made or customizable AI agents for a variety of enterprise applications. However, getting an AI agent to complete tasks for you isn’t as obvious as a simple prompt, like with chatgpt. Pre-built agents often won’t do a task with the level of discernment and specificity you may need.
So why not build one from scratch? Simply put, for most businesses, this isn’t all that feasible. You’ll either need to get someone on your team to learn how to build AI agents, or hire someone new for that very job. This can become time consuming and expensive. AI agents are also transforming software development by automating code generation, accelerating deployment, and streamlining workflows, making the process more efficient but still requiring technical expertise.
The solution is a balance between these two options, a best of both worlds if you will. You simply need to hire a company that will create and manage your agents for you. They carry the expertise, and in exchange, you immediately get a rental agent that has been uniquely customized by a proven service to do exactly what you need. Here at Get The Clicks, we’re aiming to soon do just that, incorporating the creation of AI agents for our clients along with our other services to help them achieve their goals. The only downside worth noting here is the requirement to continue working with a company that has created your AI agent. Don’t make a commitment like this unless you are certain the AI agent company you are working with is a good fit for you.
Safety, Quality, and Compliance
Guardrails
We’ve already discussed this a bit previously, but for clarity’s sake, guardrails, along with all other parts of this section of this article, must be established. Failure to do so could result in sending the wrong messages, bad data updates, privacy issues, and wrecking customer trust. AI is like fire, a crucial human discovery/innovation, and worth treating carefully. Tread lightly.
An agent should only have access to what it needs. If you’re unsure if an agent should have access to something, do not give it access. Have humans approve risky actions. Treat an agent with inclusive instructions rather than exclusive instructions, meaning you tell an agent what to do and not what not to do, (for example, telling an agent “Perform task X in this exact way, and do not do anything else” versus “Perform task X without doing ABC). An agent should also be able to hand off a task to a human if it feels uncertain of its ability to handle an objective.
Quality Controls and Testing
You should never implement an AI agent without thoroughly testing it to ensure it does exactly what you need it to do. Test edge cases, like seeing how it handles an angry customer, missing info, unusual requests, and so on. Establish “don’t guess” policies. Add error handling as well, such as not continuing to blindly try to complete a task if the CRM is down.
Having activity logs such as what the agent did, when it did it, what data it used, and who approved it, ensures any holes in your quality controls can be plugged up.
Privacy and Data Handling
Consider the idea of limiting what kinds of information an AI agent can see, avoiding the storage of sensitive info in unnecessary places, and so on. Confidentiality is key here for your clients, and as stated previously, clients should be aware of how little or how much information is being shared with AI when they work with you.
How to Train Your AI
Training, and SOPs
Once you’ve set up an AI to be safe and ethical in its usage, you now need to train it. If a company is doing this for you, your life will be made much easier, but let’s cover the process anyway.
The process of testing your AI agent before using it doesn’t just help find issues with it. It also means teaching the AI to follow a specific set of protocols depending on the scenario. Teaching the agent how to review drafts quickly, when to give a task to a human, how to report issues, and so on, will help smooth things out.
Agents also need SOPs to be written clearly. In fact, any data the agent accesses needs to be neatly organized for processing. SOPs need to be step-by-step, with definitions if needed, established approved language, and even examples.
In case it hasn’t somehow been clearly implied by now, the process of “hiring” an AI agent still requires a large investment of your time to get the agent to do what you need, just like an actual human being. The difference isn’t that an AI agent will be ready out of the box like a chatbot or an assistant, but rather that once you’ve successfully trained the agent, you’ve achieved efficiency nirvana. You have an employee who doesn’t need to be paid, doesn’t need breaks, and gives you the control to change their behavior, train them to do something new, fire them, and so on without risk of them quitting or complaining.
Support and Maintenance
You will never simply just let an AI agent go on forever completing a goal once prompted. You’ll need to tune prompts, rules, retrieval sources, and edge case handling over time as you get more experience with said agent and locate smaller and smaller cracks in its systems.
Measurement and Optimization
KPIs, Weekly summaries, and ROI Reports
To ensure your AI agent is doing what you need it to do, key performance indicators will help you measure just how well it is working. I hope you like analysing data, because that’s what KPIs, along with the rest of this section is all about. Take a look at response times, how much work is being processed, error rates, and conversion rates. Chances are, once you get the system properly up and running, you’ll already be dumbfounded at how much of an increase in efficiency you get. The key here is to not settle for “good enough”. Figure out how you can make your agent do its job even better, be more efficient, be more accurate, and so on.
A great way to track how well your agent is working is a weekly report. The agent can provide it, or you can get someone to cook it up for you by analysing things like the agent’s KPIs. What did the agent handle? What needed approvals? What got sent to a human? Are there any measurable improvements? Should there be?
It can be useful to translate a lot of this data into business language. How many hours/dollars did the agent save you? Where was revenue increased, or better service provided, in tangible, clearly connected ways, even if anecdotal?
Monthly Tune-Ups
Every month, you should take a moment to either tune your agent, or get the company behind the agent to tune it for you. This is a great way to ensure opportunities to review behavior, update knowledge bases, improve prompts, and even expand an agent into a new workflow.
Conclusion
AI agents are at their best when you start small. Remember, focus agents onto tasks that are low-risk, repetitive, and involve large amounts of data. The hardest part to all of this is overcoming the barrier to entry. Getting an AI agent to work properly for you takes some mixture of time, money, and effort, no matter what approach you take. However, if you can overcome that, you’ll maximize your efficiency in a way that’s straight out of sci-fi media.
