What are AI Agents? How To Build AI Agent For Your Business?

Surviving in the present era of cut-throat competition while maintaining operational efficiency and boosting productivity is certainly a demanding endeavor for businesses. 

Along with evolving market trends and  rapidly advancing technology, businesses have to deal with multiple challenges. 

Do you know that: 

• 75% of employers are having difficulties in filling the vacant positions
• Inefficiencies of exciting employees are leading to 20 to 30% revenue loss per year
• 44% of a company’s staff is overburdened 
• 40% productivity is lost due to task switching 

These statistics are not only alarming but also indicative of the pressing need for businesses to recognize that relying solely on their human workforce may no longer suffice. Embracing AI agents as valuable assets can significantly enhance operational efficiency, decision-making processes, and overall business performance. 

By leveraging technologies such as LLM, artificial intelligence, and machine learning, autonomous AI agents have the potential to make a business productive, better performing, and process data accurately. 

If you haven't explored the power of AI agents for businesses and have no idea how to build it, scroll down to know every bit of it. 

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What is an AI Agent? 

AI agents are high performing autonomous robots or virtual assistants that leverage artificial intelligence to autonomously perform tasks. These intelligent agents perceive their environment, interpret data and process queries to produce intended output. 

In the corporate context, agents in AI are now considered as the most reliable helping hand that businesses can use to perform mundane jobs that consume 62% of a workday. 

Artificial agents offer businesses a valuable opportunity to optimize their workforce by handling tasks such as customer inquiries, data analysis, and standardized actions, thereby freeing up human employees for more complex and creative tasks.

Depending upon the integrated capabilities and underlying technologies stack that mainly involves  machine learning and artificial intelligence, these agents can have laudable features and characteristics such as: 

Real-time Perception 

AI agents are designed to perceive and collect key data from their corresponding environment using technologies like cameras, text inputs, and microphones. The collected data is then interpreted accurately to make sense out of it. 

Decision-making and Reasoning 

Through the adequate use of decision-making and reasoning algorithms, AI agents can help businesses to make the best and most favorable decisions. This ability is powered by core technologies such as logical reasoning, rule-based systems, reinforcement learning, and machine learning. 

Prompt Actions 

AI agents can process the available data and take prompt actions to achieve the goals. These actions can be diversified ranging from moving an object to complex virtual interactions. 

Immediate Learning-based Adaptation 

AI agents have great abilities to adapt based on their past learning. They can revise their behavior according to the feedback they get from the users or admin. They can update, modify, and upgrade their knowledge, which makes them scalable and adaptable.

In-the-moment communication 

Artificial intelligence agents are capable of engaging with humans or other agents in concurrent communication in the form of text, gestures, and speeches. They have the power to process and comprehend the natural human language and act/respond accordingly. They can even process the information provided in a query and provide an appropriate response. 

Considering all these proficiencies, it’s not erroneous to call agents in AI the best bet a business can make to have an impact on their operational fronts. 

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Types of Artificial Intelligence Agents Businesses Can Have 

There are six key types of agents in AI that businesses can have to power their businesses. 

1. Simple Reflex or SR AI Agents 

The most common artificial intelligence agents we have are Simple Reflex or SR agents that can help businesses make informed decisions using the provided data. 

They operate using condition-action rules and are easy to develop and implement. They are made up of components such as agents, actuators, sensors, and environment. 

The good thing about these types of agents is that they can discard the historic precepts while making decisions. 

Example- A temperature-controlled room with an automated air conditioning system.

2. Model-Based AI Agents

The next type of AI agent development you can engage in is model-based AI agents. These agents are best known for making quick rules-driven decisions by incorporating a deeper understanding of the surroundings. 

As they keep current situations in percept during the decision-making process, they tend to be a little more observational. Also, they are great for upgrading the internal model based on past percepts, highly flexible, and can handle complex tasks easily.

Example: Waymo, a project by Google, is used in self-driving vehicles and helped in reducing police-reported crash cases by 57%. 

3. Goal-Based AI Agents

Up next we have goal-based agents that businesses can develop to meet certain objectives. They use decision-making algorithms to understand the best line of action to learn from the surroundings. 

The best use cases of this type of artificial intelligence agents is to predict future trends, promote optimized resource allocations, automated designing, and personalized marketing. They continue to learn and refine. 

Example: A chess-playing agent in AI that aims to win the game. 

4. Utility-based AI Agents

Utility-based AI agents are highly sophisticated artificial intelligence agents that can make decisions utilizing a specific value or utility. They aim to make the most favorable actions for predefined tasks/utilities such as resource allocation, and strategic planning using their ability to ensure optimal decision-making at each repeated step. 

They can handle a wide range of problems and deliver an objective framework for decision-making. But, they require additional oversight.

Example: Anthropic Claude is an AI agent with 175 billion parameters. 

5. Learning AI Agent

Lastly, we have Learning AI agents that are used widely for acquiring knowledge and providing feedback. They have sensors to observe the surroundings and use algorithms to analyze the data. The four key components of a learning agent in AI are Learning, Critic, Performance, and Problem Generator. 

As they can evolve with time and easily convert ideas into actions, businesses can use them to gain insights into customers’ past experiences and criticize past performance. 

However, they require a great deal of investment on the development and maintenance front. But, if you take the help of AI agents consulting services, you can optimize their software development cost. 

Example: AutoGPT is a Learning AI agent developed by Significant Gravitas. It can perform market research, evaluate websites, and generate reports. 

6. Hierarchical AI Agent

Hierarchical agents in AI are a couple of individual AI agents integrated to set up a highly coordinated workflow. The AI agent at the top of the hierarchy oversees the operations of other AI agents. 

Such types of AI agents are designed to coordinate between different interlinked departments, assign and track sub-tasks, and spot operational bottlenecks. 

Example: UniPi by Google is a hierarchical AI agent that follows high-level and low-level policies for generating instructions and task execution respectively.

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Environments Where AI Agents Can Work Seamlessly 

Before you hire AI developers and assign them the duty to build fully customized agents with AI, you need to know the type of environments where these agents can operate excellently. 

• Artificial Intelligence agents can work seamlessly in a real-like virtual environment that can copy real-world situations. Such environments are perfect for training and testing tasks. 
• With the help of components such as sensors, microphones, lidar, and actuators, AI agents can perform par excellently in physical environments like warehouses, hospitals, airports, and factories. 
• Businesses operating in the retail sector can develop AI agents to monitor sales, product prices, customize promotions, notify customers, and analyze the purchase cycle of a customer. 
• AI agents for the travel industry are capable of performing activities such as trip planning, making suggestions on destinations, optimising the itinerary according to the budget, and interacting with customers in a virtual and text-based environment. 
• With high-end AI development techniques, businesses can create AI agents that can perform accurately in a wide range of in-game environments like video games, casino games, and mobile games. They can design game rules, evaluate gamers’ performance, and even create different game objectives. 
• Social media platforms such as dating apps, online communities, Facebook, and Twitter can deploy Artificial Intelligence agents to interact and collaborate with other humans and agents.  
• The finance industry can deploy autonomous AI agents to analyze stock prices, customer transactions, market risks, investments made, and detect threats.

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Forms of AI Agents 

AI agents are of mainly three kinds that we have explained next to you. 

• Single AI Agent- Can handle only specific task scenarios based on user needs.  
• Multi-AI Agents- These agents work in collaboration with other AI agents and take actions/decisions based on mutual communications.  
• Hybrid AI Agent- This category is based on human and computer interaction for decision-making. They are high-end agents capable of performing complex professional activities. 

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What AI Agents Can Do For Businesses? 

A  customized agent in AI can play multiple roles for businesses such as: 

Customer Support Agent

When integrated with NLP, an AI agent can help businesses to set a fully automated and highly responsive customer support that can handle customer queries, perform troubleshooting activities, and make 100% customized recommendations. 

Process Automation Agent

Agents in AI can streamline workflows by automating repetitive tasks, enhancing efficiency by 40%, and reducing human error in various business processes.

Employee Onboarding Agent

The HR department of a business can integrate AI agents into the hiring process, can automate interviews, sort and select the right candidate profiles, conduct training, plan orientation programs, and even automate 50% of HR operations.  

Predictive Analytics Agent

Utilizing the data analysis capabilities of Artificial Intelligence agents, businesses can predict risks & market trends, identify patterns, detect irregularities, and frame a remedy policy, based on the issues identified. 

Brand Marketing Agent

Focused on promoting brand awareness and engagement, this AI agent will ensure that businesses have fully optimised marketing campaigns. Additionally, it can monitor brand sentiment, boost customer loyalty, predict customer behaviour, manage multi-channel marketing, track the performance of a campaign, and save 40 minutes per week. 

Fraud Detection Agent

A fraud detection AI agent enables businesses to spot transactional and customer behaviour anomalies in the early stage, reducing the risks of fraud. 

It can also help in framing a risk-protection policy, automated threat notification, conducting scheduled scans, and taking primary actions. 

Quality Monitoring Agent

With the help of a quality monitoring agent, businesses can boost their defect detection abilities by up to 90%, track the root cause of degraded quality, optimize production, improve quality standards, and gain complete visibility of the production line. 

Supply Chain Operations Agent

Managing inventory, logistics, and distribution automation up to 80% are some of the benefits to bag with an AI-driven supply chain operation agent. It can identify logistics bottlenecks and recommend viable solutions to boost overall operational efficiency.

Employee Productivity Agent

By tracking performance metrics, providing feedback, and offering resources, agents in AI can boost employee efficiency by 66% for all sorts of organizations.  

Energy Management Agent

By leveraging the analytical and data interpretation skills of an energy management agent, enterprises can implement energy-saving policies across the department. 

They can automate system failure detection, optimize energy consumption, identify the energy loss areas promptly, and lower down the maintenance cost by 10% to 40%. 

Decision Support Agent

Companies have a chance to improve their decision-making abilities by 37% if they integrate a decision-making agent into their key workflow. 

This agent can integrate and analyze the sea of data based on predefined goals accurately and promptly, enabling businesses to make timely informed choices.

Lead Manager Agent

AI in lead generation is the best bet for acquiring quality leads, capturing leads from multiple platforms, sorting leads based on different goals, performing real-time lead analysis, and automating lead sourcing. 

Executive PA

Acting as a virtual assistant to executives, this AI agent can take notes, schedule meetings, communicate within the team, save 40% of the time by doing personalized weekly time, and automate various administrative tasks. 

Sales Rep Agent

An AI-based sales representative is a great asset to own for the retail industry as it can boost lead generation by 50%, automate lead conversion & sales process, free up calling time, make personalized recommendations, and empower lower-skill sales representatives. 

Legal and Compliance Agent

Ensuring adherence to regulation, automating 44% of legal work, estimating risk, maintaining ethical work ethics, and performing due diligence during mergers & acquisitions are easy with an AI-driven legal compliance agent.

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How To Build An AI Agent That Drives Growth 

By now, we’re sure that you’re aware of the power and capabilities of artificial agents in AI. Next, you should learn the basics of developing an AI agent that can handle customized tasks for you.

Here is a quick overview of building an AI agent. 

Establish your Objective 

To commence the development of AI agents, businesses first need to clarify the purpose and objective of having an AI agent in the first place. Try to clarify what you need for an AI agent app. Do you need it to sort the documents or to handle customer queries? 

This will give you a perceptive on the development and if you’re having a tough time figuring this out, you can take the help of AI consulting services. 

Select the Right Frameworks and Libraries

You need to train the fundamental AI model to process the data and make decisions. For this, you need to choose a framework and libraries that allow AI developers to streamline the development process and enable faster prototyping. 

You can take the help of leading technologies such as TensorFlow, PyTorch, and Keras. Want to try something novel? Try LangGraph for AI Agents and LLM-based applications. 

Select a Programming Language

Programming language is a crucial resource for AI agent development as it helps in the implementation of algorithms and access to specialized libraries and frameworks. 

Python is the most preferred choice for AI development as it’s simple, easy to process, and highly versatile. It can also work without any glitches with TensorFlow, PyTorch, and Keras. 

Collect Data for Training 

Your agents in AI need data to process and analyze. Hence, you must collect quality data for training the machine learning models effectively. You can use methods like crowdsourcing or gathering data from off-the-shelf datasets. Just make sure that the data is : 

• High quality
• Unbiased 
• Error-free and well-cleaned 

The quality of data will determine the accuracy and dependency of your artificial agents in AI. This is a tedious and highly skill -demanding job. If you don’t want to get overburdened, our suggestion is to hire data science services. 

Design the Fundamental Architecture 

Next, you need to design a powerful architecture for your AI agents. Make sure that the structure is highly scalable, modular, and performance-driven. Keep the architecture open for integration so that other components of AI agents can be paired seamlessly. 

Start the Model Training

Once adequate data is collected and the basic architecture is ready, it’s time to train the AI agent model. 

At this stage, AI developers often engage in activities such as data feeding in the model, creating the environment for the AI agent, implementing the learning experience, optimizing the decision-making abilities, and pulling all the components together to commence the training. 

To achieve perfection at this stage, it’s important to: 

• Select an appropriate model out of options like random forests, neural networks, and decision trees 
• Confirm and validate the model's functioning using validation data 
• Ensure the model can learn continuously 

Deployment of AI Agent Model 

After successful model training, it’s time to deploy it using tools like serverless platform, Docker, WebAssemble, or Kubernetes. You can choose the deployment ecosystem according to your requirements.  

Through the deployment, AI developers have to containerize the model and its linked components. In addition, you need to:

• Refine and optimize the model 
• Create APIs to facilitate communication 
• Ensure that the deployment environment is fully secured and has adequate privacy standards 
• Set up the user-interfaces and interaction mechanism for users 

Test The Model 

To make sure your agents in AI perform accurately and make decisions wisely, you need to make sure that the functional model is free from any bugs, and errors, and is not behaving inappropriately. 

Don’t forget to conduct the user acceptance testing to make sure that the model can handle the user needs and expectations. 

Monitoring and Optimization 

Post the deployment, you need to make sure that you’re continually observing the performance of your artificial intelligence agents. 

You need to feed new data and create extra user interaction points. Additionally, we recommend you regularly update the underlying structure and scale it according to your business needs. This will warrant that the agents in AI can grow with your growing needs. 

These steps will help you have a smooth AI agent development process and have a fully customized AI agent that will support your operations on multiple fronts. 

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Embark on an AI Agent Development Journey With Ampcome  

With autonomous AI agents, businesses can automate, delegate, optimize, and streamline a wide range of activities while saving hours of work, provided you build an AI agent the right way. 

That is why you need someone who's an expert. Ampcome is a leading AI development company that uses an advanced technology stack for AI applications that includes Doker, Grop AI Chips, MySQL, PostgreSQL, SQL Server, Gen AI, LLM, and many more. 

Our team of AI developers has a comprehensive understanding of avant AI models, advanced technology stack technologies, and architectures, ensuring the delivery of high-quality AI agents. 

We can help various businesses to have customized AI agents to meet their unique needs, allowing them to become more reliable, highly functional, sidestep functional delays, and downsize menaces. 

Are you still overburdening yourself with mundane work?

Get free AI agent consultation to understand their true powers. Make a swift move before your peers do and lead the market, govern your users, and have a smooth workflow.