With the global market projected to reach USD 5.40 billion by 2030, sectors such as healthcare, finance, and eCommerce are embracing automation to drive efficiency and growth.
Pre-built AI agents handle repetitive tasks, analyze data, and make decisions, freeing your team to focus on what matters.
In this guide, we'll explore how AI agents work and how you can use them to streamline your operations and drive business success.
What are AI agents?
AI agents, also known as intelligent agents, are autonomous software systems designed to observe, make decisions, and act independently without requiring constant human intervention.
AI agents employ advanced artificial intelligence techniques, including machine learning and natural language processing, to analyze data, make informed decisions, and automate processes across various industries.
Open AI agents are a prominent example of intelligent systems that can observe, make decisions, and act without constant human intervention.
Powered by OpenAI's advanced language models, such as GPT, these agents are designed to process vast amounts of data, make decisions based on context, and interact naturally with users through text or voice.
AI agents vs. traditional AI tools
Unlike traditional AI tools that follow predefined rules, LLM agents leverage the power of machine learning and vast datasets to understand and generate text that is human-like.
This enables them to engage in more dynamic, context-aware conversations, making them ideal for tasks such as content creation, customer support, and lead generation.
Here's how they stack up:
| Feature | Traditional AI tools | AI agents |
| How do they operate | Wait for user commands | Act independently, without prompts |
| Decision-making | Follow the set rules | Analyze data and decide in real time |
| Level of automation | Task-specific (isolated automation) | End-to-end process execution |
| Learning ability | Static – no learning | Learns from your existing data to get smarter over time |
Unlike traditional software, autonomous agents act independently, learning from real-time data to drive business growth without constant supervision.
Let's now learn how AI agents work.
How AI agents work (and what makes them smart)
The AI agents framework forms the backbone of how these agents operate. It defines the algorithms, data models, and decision-making processes used by the agents.
Here's how AI agents operate:
1. Data collection
AI agents start by gathering data from various sources, including your CRM, website activity, chat logs, emails, and other relevant information.
Using advanced natural language processing (NLP), they analyze unstructured data, such as customer conversations and support tickets, in real-time.
2. Analysis and decision-making
AI agents excel at data processing, gathering data from various enterprise systems, including CRM, ERP, and marketing platforms.
They process it using machine learning models. These models help analyze patterns, detect anomalies, and assess situations related to business goals, enabling intelligent, real-time decision-making.
They analyze patterns and past interactions, detecting anomalies, and make decisions based on both historical data and real-time insights to align with business goals.
For instance, when a lead repeatedly visits your pricing page within a single day, the AI agent identifies buying intent and triggers a follow-up action.
3. Action execution
Unlike traditional automation tools that suggest actions, AI agents execute them instantly and independently. Whether it's sending personalized emails, updating records, or triggering workflows, they handle it all independently.
For example, to be effective, sales AI agents must be deployed in environments with well-defined tasks, such as automating lead qualification, writing emails, or following up effectively. This ensures maximum output with minimal errors.
Additionally, while AI agents excel at automating tasks, they adhere to responsible AI practices to ensure human oversight when decisions involve complex judgment or ethical considerations.
Key features of AI agents
AI agents are designed to perform tasks autonomously and adapt to your business needs. Here are the key features that make them stand out:
Autonomy: AI agents perform tasks independently, from sending emails to managing workflows without requiring manual intervention.
Perception: They constantly monitor various data sources, staying aware of the context and identifying changes or opportunities.
Reasoning & decision-making: Instead of following rigid rules, AI agents weigh options and make context-driven decisions using logic and past learnings.
Learning: AI agent performance continually improves through machine learning, as agents refine their actions based on new data and feedback, resulting in more accurate decision-making and faster task execution over time.
Goal-oriented: Every action an AI agent takes is tied to a business goal, whether it's converting leads, resolving issues faster, or reducing workload. They're designed to deliver results, not just complete tasks.
Benefits of AI agents for businesses
AI agents not only enhance speed and efficiency but also transform business processes by automating repetitive tasks, optimizing workflows, and enabling smarter decision-making across departments.
Here's how they add value:
-> Increased efficiency
AI agents handle a wide range of tasks, from simple tasks like sending emails to complex processes such as analyzing customer behavior. This frees your team to focus on high-value activities that drive business growth.
For instance, AI agents for sales not only enhance efficiency but also improve the effectiveness of sales teams. By automating time-consuming tasks such as prospecting and lead follow-ups, AI agents free up valuable time for salespeople, allowing them to focus on building relationships and closing deals.
-> Better decision-making
AI agents, especially those powered by large language models, can analyze vast amounts of text-based data such as customer feedback, emails, and support tickets and deliver actionable insights instantly. This enables businesses to make more informed decisions more quickly.
-> 24/7 availability
AI agents work around the clock, managing workflows, responding to user requests, and providing support, ensuring a seamless experience for both customers and employees.
Customers get what they need. Your team stays off the burnout train.
-> Personalized customer experiences
By utilizing behavioral data, AI agents deliver relevant content and messages that align with user intent. This boosts engagement and conversions without manual effort.
AI agents excel at handling user queries by utilizing real-time data analysis, delivering fast and accurate responses that enhance customer satisfaction and decrease response times.
For example, AI agents can suggest personalized products based on a user's browsing history, thereby increasing the likelihood of a purchase.
-> Cost savings
AI agents significantly reduce operational costs. For instance, businesses can reduce customer interaction costs by up to 90% with AI-powered virtual agents, according to the Boston Consulting Group.
Additionally, AI can reduce content production costs by 95% and increase blog publication speed by 50 times, demonstrating its value in both cost savings and efficiency.
Don't just automate tasks. Automate outcomes.
Salesmate's smart flows help you close more deals, nurture leads, and delight customers.
To fully understand how AI agents deliver these results, let's take a closer look at the different types of AI agents and how each one serves a unique purpose in your business operations.
What are the main types of AI agents (and how they drive business value)
Other agents in the AI space, such as autonomous AI agents, focus on taking action without human input. However, certain agents may still require human oversight or have more specific capabilities, depending on the task at hand.
Here are the seven foundational types of AI agents:
1. Simple reflex agents
These agents respond to specific inputs by applying predefined rules. They don't analyze past data; they rely on a direct stimulus-response.
Here's an example: A factory sensor that shuts down a machine when it detects overheating. It reacts, but doesn't analyze past data.
2. Model-based reflex agents
Unlike simple reflex agents, these use a basic internal model of the environment to make more informed decisions, even when some information is missing.
Here's an example: A smart security camera that uses past motion patterns to distinguish between regular office activity and potential intrusions.
3. Goal-based agents
These agents choose actions based on specific goals. They plan, assess options, and take action in ways that help them achieve their target.
Here's an example: A warehouse robot that navigates changing layouts to deliver packages to the right location, even if obstacles appear along the way.
4. Utility-based agents
Utility-based agents evaluate different actions based on their potential outcomes and select the one that offers the highest overall benefit.
Here's an example: A pricing optimization engine that balances profit margins, inventory levels, and customer demand to select the ideal selling price.
5. Learning agents
These agents improve with experience. They adjust their behavior based on outcomes, new data, and feedback, becoming smarter over time.
Here's an example: A recommendation system that fine-tunes suggestions based on user behavior and engagement history to boost conversions.
6. Hierarchical agents
Hierarchical agents manage large, complex tasks by breaking them down into smaller parts. Sub-agents handle individual tasks, while the top-level agent coordinates all of them.
Here's an example: In manufacturing, a high-level agent manages production schedules while lower-level agents oversee machinery performance, supply tracking, and quality control.
7. Multi-agent systems
These systems involve multiple AI agents collaborating, often across departments or functions, to solve complex problems through coordination and the sharing of data.
Here's an example: In logistics, one agent forecasts demand, another manages routing, and others handle inventory. Together, they optimize the entire supply chain.
Each type of agent brings different strengths to the table, but the real power lies in connecting agents across departments, enabling them to share data and collaborate effectively.
Now that we understand the types of AI agents, let's examine how they are being utilized in various industries to drive business value.
9 AI use cases across industries
AI agents can manage complex workflows, from lead generation to client onboarding, ensuring seamless operation across multiple systems without requiring manual intervention.
Sixty-two percent of consumers now prefer AI digital assistants over human agents, citing quicker and more consistent responses as the primary reasons. This shift highlights the growing expectation for businesses to integrate AI agents into their customer service operations, facilitating faster and more reliable interactions.
Here are some of the AI agents' business impact examples:
Retail & eCommerce
In retail, AI agents help brands predict demand and personalize the customer experience.
For example, Zara uses AI agents to monitor sales and customer behavior, adjusting inventory in real-time to meet demand. This reduces dead stock and ensures a fast turnaround on trending items.
Beyond inventory management, AI agents recommend personalized products based on customer behavior, thereby increasing sales without requiring additional manual effort.
Finance & banking
AI agents in finance play a crucial role in fraud detection and operational efficiency. Mastercard's AI-powered fraud detection system processes billions of transactions daily, identifying anomalies and preventing fraud in real time.
AI agents also automate back-office operations, such as invoice processing and compliance, thereby reducing errors and accelerating tasks.
In banks, AI voice agents are gaining popularity. These agents can handle voice-based queries autonomously, providing instant responses to customers over the phone or via voice assistants, ensuring quick resolutions and improved customer satisfaction.
Healthcare
AI agents in healthcare improve both patient care and administrative efficiency.
For example, Navina's AI agents scan patient data to provide doctors with relevant insights before consultations, improving diagnostic accuracy and saving time.
AI agents also automate administrative tasks, such as updating electronic health records, enabling healthcare staff to focus on patient care.
Manufacturing
AI agents in manufacturing sector are revolutionizing manufacturing with predictive maintenance and quality control.
Siemens uses AI to monitor equipment health and schedule maintenance before issues arise, reducing downtime. AI agents also detect product defects in real-time, improving product quality and reducing waste.
Logistics & supply chain
Logistics leaders, such as UPS, utilize AI agents to optimize delivery routes, resulting in millions of dollars saved in fuel and reduced delivery times.
AI agents in logistics also manage inventory, anticipate demand shifts, and trigger restocking, ensuring efficient fulfillment and reducing stockouts.
Energy
In the energy sector, AI agents help optimize consumption and reduce waste. Schneider Electric uses AI to monitor energy usage across industrial facilities, adjusting controls to balance demand.
In this industry, AI agents also play a role in predictive maintenance, identifying equipment issues before they lead to failures.
Real estate
AI agents in real estate analyze vast amounts of property data to provide dynamic property valuations and personalized recommendations to buyers and sellers.
Platforms like Zillow use AI to adjust property prices based on market trends and user behavior, giving users confidence in their decisions.
AI agents also expedite processes such as tenant screening and risk detection, thereby reducing friction in property transactions.
Human resources
AI agents streamline HR tasks, from candidate screening to interview scheduling.
For example, HireVue uses AI to pre-screen candidates by analyzing speech patterns and facial cues to assess job fit.
AI agents also handle follow-ups, role matching, and internal mobility, allowing HR teams to focus on high-impact tasks.
Customer service
In customer service, conversational AI agents are gaining popularity.
These agents can handle voice-based queries autonomously, providing instant responses to customers over the phone or via voice assistants, ensuring quick resolutions and improved customer satisfaction.
For instance, AI voice agents can answer frequently asked questions, assist with product troubleshooting, and even process service requests, all without human intervention.
How to decide the right AI agent for your business
Choosing the best AI agents isn't about picking the tool with the most features. It's about finding the one that fits your business and has AI capabilities to solve a real problem, and scales with you.
Here's how to make the right choice to accomplish tasks:
1. Spot the tasks AI should own
Begin by identifying the repetitive and time-consuming tasks that your team faces daily. This may include tasks such as qualifying leads, routing support tickets, scheduling meetings, or synchronizing CRM data. These are prime opportunities for automation.
Look for specific tasks that are already structured. AI cannot optimize chaos, but if there is a clear pattern or workflow, that's where an agent can take over with minimal risk.
2. Set a realistic budget
AI tools vary in cost. Some are plug-and-play, while others need deep customization. Beyond the sticker price, consider additional fees such as setup, training, or ongoing support.
Figure out what time savings or performance lift would justify the investment. A tool that saves 10 hours a week may easily pay for itself, even if the upfront cost looks high. Think long-term value, not just short-term affordability.
3. Future-proof your business with scalable AI
What you need today isn't always what you'll need next quarter. Your AI agent should be able to handle more users, more data, or more complexity as your business grows.
Look for platforms that offer flexibility. Ask open-ended questions like:
- Can you build new workflows without having to start over?
- Can you add features without switching to a different tool?
Scalability isn't just about performance; it's about adaptability.
4. Ensure seamless integration
Integrating AI agents into your existing tech stack is crucial.
Ensure they seamlessly integrate with both your internal systems, such as CRM, ERP, and helpdesk, as well as external systems like third-party tools, APIs, and cloud platforms, creating a unified system.
Ask vendors about integration options before committing. Native integrations, APIs, and webhook support should all be on the checklist.
5. Keep it simple and intuitive
Even the most advanced AI won't help if your team struggles to utilize it. A seamless user interface is key to adoption; it should be intuitive, easy to navigate, and require minimal training, so your team can start benefiting from it right away.
Avoid tools that look powerful but require technical staff to manage day-to-day usage. Choose simplicity that enables your team to move fast.
6. Support is a must
Strong customer support, clear documentation, and access to onboarding resources significantly enhance adoption and performance.
Before making a purchase, inquire about the available support options that are offered. Is it an email-only service, or do you receive a dedicated contact? Do they offer onboarding calls? Choose a vendor that treats support as part of the product, not an afterthought.
7. Test it before full deployment
Run a pilot program on a small process or single team. Measure how it performs, how easy it is to use, and how well it fits into your workflows.
Pilots are a low-risk way to validate both the technology and your internal readiness. If it works in a small setting, you can confidently roll it out across the organization.
The best AI agent is the one that removes friction, saves time, and grows with you. Start with clarity, test with focus, and scale what delivers results.
Juggling multiple tools for marketing, sales & support?
Experience Salesmate, your AI-powered CRM to centralize all your sales, marketing, and support efforts into one intuitive platform.
You can create custom AI agents. Let's move to our next section, which discusses how to build AI agents from scratch.
How to create AI agents: A step-by-step guide
Once you've chosen the right AI agent, you may want to build your own, especially if you have unique business needs or prefer custom solutions.
Here's how you can start building your AI agents:
-> Define the task
Identify the specific task you want the AI agent to perform, such as answering customer queries or managing emails.
-> Choose an AI agent builder
Select a platform that aligns with your technical expertise and business needs. Platforms like Rasa and Botpress provide open-source frameworks that enable you to develop, train, and deploy AI agents without needing to start from scratch.
These open-source AI agents provide flexibility and can be easily customized to fit your unique business needs.
-> Design the agent
Utilize the chosen platform's interface to design the agent's workflow, integrating necessary data sources and defining its actions.
-> Train the AI agent
Feed the agent with relevant data to train it for the specific tasks. This may involve uploading documents, setting up APIs, or providing sample interactions.
-> Deploy and monitor
Once trained, deploy the agent within your business environment. Continuously monitor its performance and make adjustments as needed to ensure optimal functionality.
If you are looking for an AI agents tutorial, here is an insightful video resource I found for you.
Challenges in implementing AI agents
While AI agents offer many benefits, there are challenges:
Data privacy: Ensuring customer data is protected and compliant with laws such as GDPR.
Integration: AI agents must work seamlessly with your existing systems, which may necessitate customization.
Training: AI agents must be trained with quality data to perform well.
Human oversight: Although AI agents are autonomous, they still require human monitoring to handle complex or ethical situations.
Future of AI agents in business
The future of AI agents is incredibly promising, with advancements in autonomy, machine learning, and natural language processing, offering significant opportunities for growth and innovation. In the coming years, AI agents are expected to become even more:
Multifunctional: Capable of handling multiple tasks across different business departments, from marketing to finance.
Autonomous: They will work independently, taking over more complex decision-making processes.
Adaptive: AI agents will continually improve by learning from new data, making them more efficient and effective.
Businesses across industries will rely heavily on AI agents to scale operations, reduce costs, and enhance customer experiences.
Conclusion
While AI agents excel at automating tasks, human intervention is necessary when the AI encounters complex situations that require ethical judgment or when data is ambiguous, ensuring that decisions align with business values.
Salesmate can be your powerful AI agent platform with Sandy AI, integrating AI-powered automation to ensure efficiency and seamless growth for your business.
Frequently asked questions
1) How do AI agents learn and adapt over time?
AI agents analyze data and identify patterns. As they interact with more data, they improve their decision-making through machine learning.
2) What role do large language models (LLMs) play in AI agents?
LLMs, such as OpenAI's GPT, enable AI-powered agents to understand and generate text that resembles human language. This allows AI agents to:
- Answer customer queries naturally.
- Generate personalized emails or content.
- Handle complex conversations by understanding the context and tailoring responses accordingly.
3) Can AI agents replace human employees in certain industries?
AI agents can automate many tasks, but they won't fully replace humans. Instead, they can help AI agents automate routine tasks, such as updating records, sending follow-up emails, and managing support tickets, thereby freeing up your team to focus on higher-value activities that drive business growth.
4) What are vertical AI agents?
Vertical AI agents are specifically tailored to meet the needs of various industries. For example, in finance, they handle transactions and detect fraud. In real estate, they automate property searches and lead generation. These agents optimize industry-specific tasks more efficiently than general-purpose ones.
5) What are generative AI agents, and how are they used?
Generative AI agents are AI systems that produce new output, such as text, images, or even strategic decisions. To create new content or actions based on input data. Unlike traditional AI agents that focus on decision-making or task automation, generative AI agents produce.
6) What are some real-world applications of AI agents?
AI agents are utilized in a wide range of industries. Here are some AI agents' business applications:
- Customer support: Handle inquiries and troubleshoot issues
- Marketing: AI agents in marketing are to personalize interactions, send targeted ads, and automate email campaigns
- Healthcare: Assist in diagnosing conditions and managing patient data
- Finance: Automate fraud detection and provide financial insights
- Real estate: Automate property searches and customer interactions
7) What are no-code AI agents?
No-code AI agents empower businesses to develop and deploy AI solutions without requiring programming expertise. Make and Zapier are great platforms that allow you to build custom no-code AI agents for your business.
8) What are the top AI agents available for businesses?
The top AI agents available for businesses include platforms like OpenAI's GPT models, Salesforce's Einstein, IBM Watson, and Salesmate.
Key takeaways
With the global market projected to reach USD 5.40 billion by 2030, sectors such as healthcare, finance, and eCommerce are embracing automation to drive efficiency and growth.
Pre-built AI agents handle repetitive tasks, analyze data, and make decisions, freeing your team to focus on what matters.
In this guide, we'll explore how AI agents work and how you can use them to streamline your operations and drive business success.
What are AI agents?
AI agents, also known as intelligent agents, are autonomous software systems designed to observe, make decisions, and act independently without requiring constant human intervention.
AI agents employ advanced artificial intelligence techniques, including machine learning and natural language processing, to analyze data, make informed decisions, and automate processes across various industries.
Open AI agents are a prominent example of intelligent systems that can observe, make decisions, and act without constant human intervention.
Powered by OpenAI's advanced language models, such as GPT, these agents are designed to process vast amounts of data, make decisions based on context, and interact naturally with users through text or voice.
AI agents vs. traditional AI tools
Unlike traditional AI tools that follow predefined rules, LLM agents leverage the power of machine learning and vast datasets to understand and generate text that is human-like.
This enables them to engage in more dynamic, context-aware conversations, making them ideal for tasks such as content creation, customer support, and lead generation.
Here's how they stack up:
Unlike traditional software, autonomous agents act independently, learning from real-time data to drive business growth without constant supervision.
Let's now learn how AI agents work.
How AI agents work (and what makes them smart)
The AI agents framework forms the backbone of how these agents operate. It defines the algorithms, data models, and decision-making processes used by the agents.
Here's how AI agents operate:
1. Data collection
AI agents start by gathering data from various sources, including your CRM, website activity, chat logs, emails, and other relevant information.
Using advanced natural language processing (NLP), they analyze unstructured data, such as customer conversations and support tickets, in real-time.
2. Analysis and decision-making
AI agents excel at data processing, gathering data from various enterprise systems, including CRM, ERP, and marketing platforms.
They process it using machine learning models. These models help analyze patterns, detect anomalies, and assess situations related to business goals, enabling intelligent, real-time decision-making.
They analyze patterns and past interactions, detecting anomalies, and make decisions based on both historical data and real-time insights to align with business goals.
For instance, when a lead repeatedly visits your pricing page within a single day, the AI agent identifies buying intent and triggers a follow-up action.
3. Action execution
Unlike traditional automation tools that suggest actions, AI agents execute them instantly and independently. Whether it's sending personalized emails, updating records, or triggering workflows, they handle it all independently.
For example, to be effective, sales AI agents must be deployed in environments with well-defined tasks, such as automating lead qualification, writing emails, or following up effectively. This ensures maximum output with minimal errors.
Additionally, while AI agents excel at automating tasks, they adhere to responsible AI practices to ensure human oversight when decisions involve complex judgment or ethical considerations.
Key features of AI agents
AI agents are designed to perform tasks autonomously and adapt to your business needs. Here are the key features that make them stand out:
Autonomy: AI agents perform tasks independently, from sending emails to managing workflows without requiring manual intervention.
Perception: They constantly monitor various data sources, staying aware of the context and identifying changes or opportunities.
Reasoning & decision-making: Instead of following rigid rules, AI agents weigh options and make context-driven decisions using logic and past learnings.
Learning: AI agent performance continually improves through machine learning, as agents refine their actions based on new data and feedback, resulting in more accurate decision-making and faster task execution over time.
Goal-oriented: Every action an AI agent takes is tied to a business goal, whether it's converting leads, resolving issues faster, or reducing workload. They're designed to deliver results, not just complete tasks.
Benefits of AI agents for businesses
AI agents not only enhance speed and efficiency but also transform business processes by automating repetitive tasks, optimizing workflows, and enabling smarter decision-making across departments.
Here's how they add value:
-> Increased efficiency
AI agents handle a wide range of tasks, from simple tasks like sending emails to complex processes such as analyzing customer behavior. This frees your team to focus on high-value activities that drive business growth.
For instance, AI agents for sales not only enhance efficiency but also improve the effectiveness of sales teams. By automating time-consuming tasks such as prospecting and lead follow-ups, AI agents free up valuable time for salespeople, allowing them to focus on building relationships and closing deals.
-> Better decision-making
AI agents, especially those powered by large language models, can analyze vast amounts of text-based data such as customer feedback, emails, and support tickets and deliver actionable insights instantly. This enables businesses to make more informed decisions more quickly.
-> 24/7 availability
AI agents work around the clock, managing workflows, responding to user requests, and providing support, ensuring a seamless experience for both customers and employees.
Customers get what they need. Your team stays off the burnout train.
-> Personalized customer experiences
By utilizing behavioral data, AI agents deliver relevant content and messages that align with user intent. This boosts engagement and conversions without manual effort.
AI agents excel at handling user queries by utilizing real-time data analysis, delivering fast and accurate responses that enhance customer satisfaction and decrease response times.
For example, AI agents can suggest personalized products based on a user's browsing history, thereby increasing the likelihood of a purchase.
-> Cost savings
AI agents significantly reduce operational costs. For instance, businesses can reduce customer interaction costs by up to 90% with AI-powered virtual agents, according to the Boston Consulting Group.
Additionally, AI can reduce content production costs by 95% and increase blog publication speed by 50 times, demonstrating its value in both cost savings and efficiency.
Don't just automate tasks. Automate outcomes.
Salesmate's smart flows help you close more deals, nurture leads, and delight customers.
To fully understand how AI agents deliver these results, let's take a closer look at the different types of AI agents and how each one serves a unique purpose in your business operations.
What are the main types of AI agents (and how they drive business value)
Other agents in the AI space, such as autonomous AI agents, focus on taking action without human input. However, certain agents may still require human oversight or have more specific capabilities, depending on the task at hand.
Here are the seven foundational types of AI agents:
1. Simple reflex agents
These agents respond to specific inputs by applying predefined rules. They don't analyze past data; they rely on a direct stimulus-response.
Here's an example: A factory sensor that shuts down a machine when it detects overheating. It reacts, but doesn't analyze past data.
2. Model-based reflex agents
Unlike simple reflex agents, these use a basic internal model of the environment to make more informed decisions, even when some information is missing.
Here's an example: A smart security camera that uses past motion patterns to distinguish between regular office activity and potential intrusions.
3. Goal-based agents
These agents choose actions based on specific goals. They plan, assess options, and take action in ways that help them achieve their target.
Here's an example: A warehouse robot that navigates changing layouts to deliver packages to the right location, even if obstacles appear along the way.
4. Utility-based agents
Utility-based agents evaluate different actions based on their potential outcomes and select the one that offers the highest overall benefit.
Here's an example: A pricing optimization engine that balances profit margins, inventory levels, and customer demand to select the ideal selling price.
5. Learning agents
These agents improve with experience. They adjust their behavior based on outcomes, new data, and feedback, becoming smarter over time.
Here's an example: A recommendation system that fine-tunes suggestions based on user behavior and engagement history to boost conversions.
6. Hierarchical agents
Hierarchical agents manage large, complex tasks by breaking them down into smaller parts. Sub-agents handle individual tasks, while the top-level agent coordinates all of them.
Here's an example: In manufacturing, a high-level agent manages production schedules while lower-level agents oversee machinery performance, supply tracking, and quality control.
7. Multi-agent systems
These systems involve multiple AI agents collaborating, often across departments or functions, to solve complex problems through coordination and the sharing of data.
Here's an example: In logistics, one agent forecasts demand, another manages routing, and others handle inventory. Together, they optimize the entire supply chain.
Each type of agent brings different strengths to the table, but the real power lies in connecting agents across departments, enabling them to share data and collaborate effectively.
Now that we understand the types of AI agents, let's examine how they are being utilized in various industries to drive business value.
9 AI use cases across industries
AI agents can manage complex workflows, from lead generation to client onboarding, ensuring seamless operation across multiple systems without requiring manual intervention.
Sixty-two percent of consumers now prefer AI digital assistants over human agents, citing quicker and more consistent responses as the primary reasons. This shift highlights the growing expectation for businesses to integrate AI agents into their customer service operations, facilitating faster and more reliable interactions.
Here are some of the AI agents' business impact examples:
Retail & eCommerce
In retail, AI agents help brands predict demand and personalize the customer experience.
For example, Zara uses AI agents to monitor sales and customer behavior, adjusting inventory in real-time to meet demand. This reduces dead stock and ensures a fast turnaround on trending items.
Beyond inventory management, AI agents recommend personalized products based on customer behavior, thereby increasing sales without requiring additional manual effort.
Finance & banking
AI agents in finance play a crucial role in fraud detection and operational efficiency. Mastercard's AI-powered fraud detection system processes billions of transactions daily, identifying anomalies and preventing fraud in real time.
AI agents also automate back-office operations, such as invoice processing and compliance, thereby reducing errors and accelerating tasks.
In banks, AI voice agents are gaining popularity. These agents can handle voice-based queries autonomously, providing instant responses to customers over the phone or via voice assistants, ensuring quick resolutions and improved customer satisfaction.
Healthcare
AI agents in healthcare improve both patient care and administrative efficiency.
For example, Navina's AI agents scan patient data to provide doctors with relevant insights before consultations, improving diagnostic accuracy and saving time.
AI agents also automate administrative tasks, such as updating electronic health records, enabling healthcare staff to focus on patient care.
Manufacturing
AI agents in manufacturing sector are revolutionizing manufacturing with predictive maintenance and quality control.
Siemens uses AI to monitor equipment health and schedule maintenance before issues arise, reducing downtime. AI agents also detect product defects in real-time, improving product quality and reducing waste.
Logistics & supply chain
Logistics leaders, such as UPS, utilize AI agents to optimize delivery routes, resulting in millions of dollars saved in fuel and reduced delivery times.
AI agents in logistics also manage inventory, anticipate demand shifts, and trigger restocking, ensuring efficient fulfillment and reducing stockouts.
Energy
In the energy sector, AI agents help optimize consumption and reduce waste. Schneider Electric uses AI to monitor energy usage across industrial facilities, adjusting controls to balance demand.
In this industry, AI agents also play a role in predictive maintenance, identifying equipment issues before they lead to failures.
Real estate
AI agents in real estate analyze vast amounts of property data to provide dynamic property valuations and personalized recommendations to buyers and sellers.
Platforms like Zillow use AI to adjust property prices based on market trends and user behavior, giving users confidence in their decisions.
AI agents also expedite processes such as tenant screening and risk detection, thereby reducing friction in property transactions.
Human resources
AI agents streamline HR tasks, from candidate screening to interview scheduling.
For example, HireVue uses AI to pre-screen candidates by analyzing speech patterns and facial cues to assess job fit.
AI agents also handle follow-ups, role matching, and internal mobility, allowing HR teams to focus on high-impact tasks.
Customer service
In customer service, conversational AI agents are gaining popularity.
These agents can handle voice-based queries autonomously, providing instant responses to customers over the phone or via voice assistants, ensuring quick resolutions and improved customer satisfaction.
For instance, AI voice agents can answer frequently asked questions, assist with product troubleshooting, and even process service requests, all without human intervention.
How to decide the right AI agent for your business
Choosing the best AI agents isn't about picking the tool with the most features. It's about finding the one that fits your business and has AI capabilities to solve a real problem, and scales with you.
Here's how to make the right choice to accomplish tasks:
1. Spot the tasks AI should own
Begin by identifying the repetitive and time-consuming tasks that your team faces daily. This may include tasks such as qualifying leads, routing support tickets, scheduling meetings, or synchronizing CRM data. These are prime opportunities for automation.
Look for specific tasks that are already structured. AI cannot optimize chaos, but if there is a clear pattern or workflow, that's where an agent can take over with minimal risk.
2. Set a realistic budget
AI tools vary in cost. Some are plug-and-play, while others need deep customization. Beyond the sticker price, consider additional fees such as setup, training, or ongoing support.
Figure out what time savings or performance lift would justify the investment. A tool that saves 10 hours a week may easily pay for itself, even if the upfront cost looks high. Think long-term value, not just short-term affordability.
3. Future-proof your business with scalable AI
What you need today isn't always what you'll need next quarter. Your AI agent should be able to handle more users, more data, or more complexity as your business grows.
Look for platforms that offer flexibility. Ask open-ended questions like:
Scalability isn't just about performance; it's about adaptability.
4. Ensure seamless integration
Integrating AI agents into your existing tech stack is crucial.
Ensure they seamlessly integrate with both your internal systems, such as CRM, ERP, and helpdesk, as well as external systems like third-party tools, APIs, and cloud platforms, creating a unified system.
Ask vendors about integration options before committing. Native integrations, APIs, and webhook support should all be on the checklist.
5. Keep it simple and intuitive
Even the most advanced AI won't help if your team struggles to utilize it. A seamless user interface is key to adoption; it should be intuitive, easy to navigate, and require minimal training, so your team can start benefiting from it right away.
Avoid tools that look powerful but require technical staff to manage day-to-day usage. Choose simplicity that enables your team to move fast.
6. Support is a must
Strong customer support, clear documentation, and access to onboarding resources significantly enhance adoption and performance.
Before making a purchase, inquire about the available support options that are offered. Is it an email-only service, or do you receive a dedicated contact? Do they offer onboarding calls? Choose a vendor that treats support as part of the product, not an afterthought.
7. Test it before full deployment
Run a pilot program on a small process or single team. Measure how it performs, how easy it is to use, and how well it fits into your workflows.
Pilots are a low-risk way to validate both the technology and your internal readiness. If it works in a small setting, you can confidently roll it out across the organization.
The best AI agent is the one that removes friction, saves time, and grows with you. Start with clarity, test with focus, and scale what delivers results.
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You can create custom AI agents. Let's move to our next section, which discusses how to build AI agents from scratch.
How to create AI agents: A step-by-step guide
Once you've chosen the right AI agent, you may want to build your own, especially if you have unique business needs or prefer custom solutions.
Here's how you can start building your AI agents:
-> Define the task
Identify the specific task you want the AI agent to perform, such as answering customer queries or managing emails.
-> Choose an AI agent builder
Select a platform that aligns with your technical expertise and business needs. Platforms like Rasa and Botpress provide open-source frameworks that enable you to develop, train, and deploy AI agents without needing to start from scratch.
These open-source AI agents provide flexibility and can be easily customized to fit your unique business needs.
-> Design the agent
Utilize the chosen platform's interface to design the agent's workflow, integrating necessary data sources and defining its actions.
-> Train the AI agent
Feed the agent with relevant data to train it for the specific tasks. This may involve uploading documents, setting up APIs, or providing sample interactions.
-> Deploy and monitor
Once trained, deploy the agent within your business environment. Continuously monitor its performance and make adjustments as needed to ensure optimal functionality.
If you are looking for an AI agents tutorial, here is an insightful video resource I found for you.
Challenges in implementing AI agents
While AI agents offer many benefits, there are challenges:
Data privacy: Ensuring customer data is protected and compliant with laws such as GDPR.
Integration: AI agents must work seamlessly with your existing systems, which may necessitate customization.
Training: AI agents must be trained with quality data to perform well.
Human oversight: Although AI agents are autonomous, they still require human monitoring to handle complex or ethical situations.
Future of AI agents in business
The future of AI agents is incredibly promising, with advancements in autonomy, machine learning, and natural language processing, offering significant opportunities for growth and innovation. In the coming years, AI agents are expected to become even more:
Multifunctional: Capable of handling multiple tasks across different business departments, from marketing to finance.
Autonomous: They will work independently, taking over more complex decision-making processes.
Adaptive: AI agents will continually improve by learning from new data, making them more efficient and effective.
Businesses across industries will rely heavily on AI agents to scale operations, reduce costs, and enhance customer experiences.
Conclusion
While AI agents excel at automating tasks, human intervention is necessary when the AI encounters complex situations that require ethical judgment or when data is ambiguous, ensuring that decisions align with business values.
Salesmate can be your powerful AI agent platform with Sandy AI, integrating AI-powered automation to ensure efficiency and seamless growth for your business.
Frequently asked questions
1) How do AI agents learn and adapt over time?
AI agents analyze data and identify patterns. As they interact with more data, they improve their decision-making through machine learning.
2) What role do large language models (LLMs) play in AI agents?
LLMs, such as OpenAI's GPT, enable AI-powered agents to understand and generate text that resembles human language. This allows AI agents to:
3) Can AI agents replace human employees in certain industries?
AI agents can automate many tasks, but they won't fully replace humans. Instead, they can help AI agents automate routine tasks, such as updating records, sending follow-up emails, and managing support tickets, thereby freeing up your team to focus on higher-value activities that drive business growth.
4) What are vertical AI agents?
Vertical AI agents are specifically tailored to meet the needs of various industries. For example, in finance, they handle transactions and detect fraud. In real estate, they automate property searches and lead generation. These agents optimize industry-specific tasks more efficiently than general-purpose ones.
5) What are generative AI agents, and how are they used?
Generative AI agents are AI systems that produce new output, such as text, images, or even strategic decisions. To create new content or actions based on input data. Unlike traditional AI agents that focus on decision-making or task automation, generative AI agents produce.
6) What are some real-world applications of AI agents?
AI agents are utilized in a wide range of industries. Here are some AI agents' business applications:
7) What are no-code AI agents?
No-code AI agents empower businesses to develop and deploy AI solutions without requiring programming expertise. Make and Zapier are great platforms that allow you to build custom no-code AI agents for your business.
8) What are the top AI agents available for businesses?
The top AI agents available for businesses include platforms like OpenAI's GPT models, Salesforce's Einstein, IBM Watson, and Salesmate.
Juhi Desai
Content WriterJuhi is a passionate writer and reader. She is working with the team of content creators at Salesmate. Always seeking to learn something new, Juhi has an optimistic approach towards life. When she is not writing you can find her with a book and a coffee by her side.