The Benefits and Challenges of AI Agent Implementation

Artificial intelligence agents have the potential to revolutionize businesses’ workflows, task management, and creative processes. The global autonomous AI and autonomous agent market worth was estimated at 4.8 billion USD in 2023 and, growing at a staggering CAGR of 43%, should hit $28.5 billion by 2028.

You may see effective AI agents in the business sectors of finance, transportation, manufacturing, healthcare, education, etc. For example, InnerAI, developed by Onix, is a meditation app.

 

The AI solution generates unique texts according to each user’s requirements and instantly converts them into audio for personalized meditation experiences.

InnerAI, made by Onix, creates mindfulness sessions based on parameters entered by users.

 

Read also: AI in Mental Health: Use Cases and App Ideas to Watch

 

If you are contemplating AI agent implementation for your business, this article will help you form an understanding of

 

• AI agents’ characteristics and components
• major types of AI agents and how they work
• possible applications in business, with real-life AI agents examples
• the basics of AI agent development and implementation 

 

If you want to tap into the benefits of AI agents right now, please don’t hesitate to contact Onix!

Characteristics and Components of AI Agents

We can define artificial intelligence agents as AI-powered software that performs specific tasks on behalf of individuals or organizations with as little direct human intervention as possible.

 

An AI agent operates in a physical or digital environment with which it interacts through a physical or software interface. AI agents collect and analyze data from their environments to make decisions, respond to stimuli in the environment, or take actions to achieve user-defined strategic goals.

 

Depending on their function, AI agents can incorporate various AI algorithms or machine learning (ML) techniques.

 

For example, Onix utilized technologies based on computer vision, object detection, image segmentation, and image processing in a drone and satellite AI solution for mapping and geographic information systems (GIS).

 

The software detects buildings, roads, rivers, forests, and fields, recognizes their characteristics, puts them on a generated map, and can detect changes over time through comparisons with previous observations.

 Onix’s AI-based mapping and GIS solution generates maps using images from drones or satellites.

 

Onix builds and trains ML models for purposes as varied as

 

• image classification
• language identification
• news categorization
• social media content analysis
• social media sentiment analysis
• translation of graphic novels and webtoons into multiple languages
• comic strips animations
• image denoising in info-communication systems
• face recognition and manipulations in livestreams
• green energy output prediction

Read also: 26 Real-World Image Classification Use Cases Across 6 Industries

The following characteristics make AI agents different from other software:

 

• Goal-oriented behavior. All AI agents are programmed to achieve specific objectives that can be predefined or dynamically determined by the context.
• Autonomy. AI agents can make decisions and act to achieve the set goals without constant human intervention.
• Perception. They use triggers to collect data from their environments, e.g., data streams.
• Reasoning. AI agents process data and interpret information to determine the best course of action.
• Reactivity. AI agents can respond to environmental stimuli or changes in a timely fashion.
• Proactivity. They can also take initiative in performing tasks towards a predefined goal.
• Social ability. AI agents can also connect and communicate with other AI agents or humans to accomplish tasks collaboratively.
• Ability to adapt and improve. The integration of technologies like large language models (LLMs) enables AI agents to continuously learn from interactions, experiences, and feedback to improve performance over time. If an AI agent perceives changes in the environment or gets new inputs, its behavior adjusts accordingly.

 

For instance, Onix’s AI Chef, an AI-powered nutritional app, becomes better at recommending dishes as it learns each user’s preferences and feedback. 

Onix’s AI Chef transforms dieting and meal planning into a creative odyssey. 

 

Read also: Transform Your Business with Onix’s Chatbot Development
 

AI agent architecture may include the following components or modules:

 

1)Perception
2)Knowledge base
3)Processor
4)Actuator
5)Learning system

Let’s take a look at each AI agent component:

 

Perception/profiling module

The perception module is an AI agent’s data acquisition system. It gathers raw data from the environment through sensors or data intake mechanisms, such as cameras and microphones. The input may include text, audio, images, tactile information, radio frequencies, etc. 

For instance, customer service AI agents process incoming messages, customer profile details, geolocation, time and date information, chat history, etc. A marketing/sales AI assistant relies on customer interaction data and transaction records to identify client preferences, potential leads, and market trends.

 

Knowledge base/memory module

This module organizes and stores the AI agent’s initial understanding of the environment and the rules, patterns, past experiences, and facts crucial for its analysis and decision-making process.

 

The KB enables the agent to recall previous interactions, learn from them, and use this knowledge to make decisions. As intelligent agents learn, their KB expands over time. 

For instance, self-driving cars’ memory modules contain traffic laws and local regulations, and virtual assistants “remember” user preferences and frequently asked questions.

 

Analysis and decision-making/planning module (aka processor)

The processor (also called task creator or proxy agent) integrates data from the perception and memory modules and utilizes ML and AI algorithms to draw insights from the data, determine the optimal course of action, determine and sequence essential tasks, issue commands to the actuators, or devise the best strategies to achieve the agent’s goals.

 

For example, a delivery routing assistant may create optimal routes and schedules by considering current traffic data, delivery priorities, and vehicle capacities.

The processor can use optimization algorithms, search methods, rule-based logic, neural networks, predictive models, or other tools that can make decisions.

 

Actuator/action execution module

This module is responsible for converting the processor’s decisions into specific outcomes – interactions with the environment or executable commands for output devices that further the AI agent’s goal in the real world. 

Some examples of AI agents’ actuators include text generators, voice synthesizers, API (application programming interface) integrations, and systems that send emails and text messages.

For instance, integration of an AI agent with various APIs enables it to retrieve or update information in an enterprise’s external systems, such as its enterprise resource planning (ERP) and customer relationship management (CRM) software, and industry-specific systems, such as a travel business’s booking website, hotel property management, or doctor appointment booking and hospital management systems. 

The action execution module also determines the task completion, monitors outcomes, and provides feedback for further adjustments.

 

Learning system (adaptation mechanism) 

This component is present only in the most advanced types of agents in artificial intelligence. It enables them to continuously improve performance and adapt to new situations by learning from past interactions with the environment.

 

A learning system may utilize different techniques and strategies:

 

• Supervised learning – agents learn from labeled examples to predict outcomes or take action.
• Unsupervised learning – agents detect patterns and relationships in data without explicit instructions.
• Reinforcement learning – agents learn by receiving rewards or penalties for their actions.

 

The structure of AI agents varies depending on their tasks. For instance, a smart home system may comprise only sensors, a simple processor, and actuators, while a self-driving car incorporates all the above components.

 

The Types of AI Agents

Researchers, developers, and consumers distinguish the types of agents in artificial intelligence by their

 

• need for interaction with humans
• level of independence or collaboration required
• capacity for learning
• complexity of the tasks they can handle
• the jobs they perform

… and other criteria. For instance, artificial intelligence agents can be physical, e.g., a smart thermostat, or software-based, like virtual personal assistants.

 

This table provides a simple classification of AI agents based on their relationship with environments.

 

 

Below, you can find some details of each AI agent type:

 

Simple-reflex/reactive AI agents

These agents, the simplest type, use pre-programmed rules. They respond to a stimulus detected by one or more sensors by interpreting it, making a decision, and producing output.

 

Without a memory module, they can’t consider past experiences or possible consequences. If an agent’s environment changes, it won’t adapt unless someone modifies the rules.

 

It’s best to deploy AI agents of this type in static environments, e.g., offices and homes, to help with simple, predictable, and repetitive tasks.

 

Model-based reflex AI agents

Unlike simple-reflex agents, these AI agents gather and consider information about the environment and how their actions affect it. This enables them to improve decision-making over time. 

 

The ability to adapt their model to changes lets these agents operate in dynamic environments. For instance, they can run predictive models to assess applicants’ loan eligibility.

 

Robot vacuum cleaners continuously detect obstacles, dirt, and surface types, adjust suction power, and adapt to a particular home layout and changes to it.

 

Read also: AR Vacuum Cleaner Development: Inspired by Dyson

 

Onix also has experience in such AI agent development. An iOS application for the skincare and cosmetics industry helps users find safe beauty products best suited to their unique skin and tastes. The AI agent’s model maps out the following actions:

 

• First, the AI analyzes a user’s registration details and specified skincare requirements; 
• The AI generates personalized skincare recommendations;
• Using current scientific research data and analyzing relevant products’ formulas, the AI checks them for toxic or hazardous ingredients;
• The AI generates a scientifically substantiated selection of eco-friendly ingredients and products perfectly meeting the user’s needs;
• Finally, the app helps compare different products.

The skincare & cosmetics analysis app built by Onix selects the most suitable products for each user.

 

Goal-based/deliberative AI agents

A goal-based AI agent can formulate goals, assess multiple strategies and choose the best one for achieving a specific goal, list necessary sub-tasks, act to perform them, assess whether these actions move it closer to the end-goal, and repeat until the goal is reached. 

 

The superior reasoning skills make AI agents of this type suitable for more complex environments and activities that require strategic planning, from board games to robots in manufacturing and exploration missions.

 

Hybrid AI agents

This type combines the benefits of AI agents described above. Where a quick response to a signal is needed, hybrid agents follow pre-programmed rules.

 

Decisions based on real-time data are made using a deliberative approach. This allows systems to react to predictable situations promptly and to plan and make decisions when conditions change.

Hybrid AI agents examples include autonomous cars whose AI systems plan routes and navigate on the roads and advanced robotic systems that can adjust to changing environments if needed.

 

Utility-based AI agents

These AI agents evaluate possible courses of action and choose the one that can lead to the end-goal in the most cost-efficient, fastest, or sustainable fashion (or another priority programmed by the owner).

Such AI agent implementation may help optimize household energy efficiency, city traffic, or various organizations’ productivity, logistics, electricity consumption, etc.

 

Learning AI agents

Learning agents improve over time thanks to

 

• A problem generator creating tests for environment exploration
• Learning elements to improve the agent’s knowledge and strategies
• A performance element actuating the knowledge for decision-making
• A critic evaluating the agent’s actions and providing feedback

 

Learning AI agents can also adapt without manual rule reprogramming. This ensures their efficiency in dynamic environments, e.g., with varied consumers and changing preferences.  

 

Marketplaces, online stores, travel websites, education and streaming platforms deploy AI agents that continuously improve personalized recommendations and offers based on users’ preferences, purchase patterns, seasonal trends, etc.

 

Read also: How AI Customization Drives Business Growth  

The need for human intervention divides artificial intelligence agents into:

 

1. Autonomous AI agents. These advanced systems can perform their jobs with minimal human intervention or without it. They can emulate human cognitive processes, strategic planning, self-reflection, and learning from previous actions and errors. They can focus on a single basic task or be able to decompose large and complex tasks into manageable segments, handle sequential tasks, and iterate.

 

2. Hybrid AI agents. These agents perform tasks requiring both computational power and human insights to address multifaceted challenges.

 

For instance, Onix applied the latter approach for a solution recognizing product styles and categories using data obtained through Apify. Firstly, the solution incorporates standard Topic Modeling – an unsupervised ML algorithm that finds groups of words within a body of text.

 

It automatically detects hidden themes in texts but doesn’t allow controlling categories. The second option, search by criteria like style or type of clothing, enables users to set specific rules for classification. This hybrid approach allowed us to achieve more accurate results. 

It’s also easy to distinguish the types of AI agents by their form and collaboration capabilities.

 

 

AI agents can also be classified based on the jobs they specialize in. Here are several specialized AI agents examples:

 

Creative agents

Creative AI agents generate content, assisting designers, marketers, and others in creating and iterating storyboards, illustrations, presentations, documents, etc.

For instance, Onix has built a system that automates the creation of presentations based on large data sets. The solution accepts XLSX files and uses Pandas to process the data and an LLM to generate the presentation text. This saves time and improves the consistency of presentations.

 

Code agents

ML and natural language processing (NLP) enable these AI tools to understand the context and provide assistance tailored to software developer needs, such as code generation, repetitive task automation, and quality assurance.

 

Data agents

Data AI agents can search, analyze, and summarize repositories of documents, video, audio, and other information to extract valuable insights, answer questions, send real-time notifications, etc.

For example, Onix has trained an LLM-based chatbot to discuss political, historical, and social topics related to Ukraine’s modern history.

 

Real also: Ukraine: IT Outsourcing Rates and Other Reasons to Choose It

 

Security agents

Security AI agents utilize data and intelligence to deliver insights and swift incident response for cybersecurity purposes.

 

They safeguard businesses’ data, analyze large volumes of malicious code, automate monitoring, identify and address threats, summarize findings and explain detected issues, promptly recommend immediate next steps and remediation playbooks, etc.

Learn more: Machine Learning in Fraud Detection

 

Onix also developed an AI-based solution for crowd behavior pattern recognition that can be used for security improvements, e.g., to detect threats and risks and issue alerts in real time.

Onix built a solution for crowd video analysis that can improve public space and event security.

 

Read also: Deepfake Threats: How to Protect Your Business from AI's Dark Side

 

Employee agents

These AI agents are designed to enhance employees’ productivity and facilitate collaboration by automating repetitive tasks, assisting in planning and communications, and more.

Here is a fresh example of Onix’s work – a system for organizing and systematizing documents. The AI agent is capable of:

 

1. reading documents stored in the cloud, analyzing their contents, and classifying them
2. extracting information of interest to the client from each document (e.g., the name of a specific product, manufacturer, technical characteristics of the product, document creation date, etc.)
3. generalizations of results for several documents, generating summary reports in the form of spreadsheets

The AI agent selects the type of local and free LLM with which to perform a particular analysis on its own.

 

Use Cases of AI Agents in Various Industries

96% of executives reportedly see the potential of AI agents in the business in the coming three years. 

 

Here are some industries and business processes most likely to leverage the benefits of AI agents.

 

Customer service

Customer AI agents are programmed to interact with customers and to know precisely when to transition to a human agent. They can be trained for specific subject matters and languages and operate across various channels.

Read also: How AI Technology Will Transform Customer Engagement

For example, eCommerce customer service departments can employ self-learning chatbots to automate responses to queries about products, tailored product recommendations, order tracking, and refunds management, becoming more helpful over time.

Onix also built an enterprise chatbot based on Rasa. This conversational AI platform facilitates personalized, automated interactions with customers at scale. We used SpacyNLP to improve recognition and response to requests. 

 

Read also: ChatGPT in Your Application: Opportunities & Integration Tips

More advanced AI agents go well beyond chatbots to handle more complex tasks. They can resolve customers’ problems, troubleshoot technical issues, manage ticket backlogs, automatically send CSAT surveys, gather information, and more.

 

Behind the curtains, companies can deploy AI agents to review their CRM and other customer data for Know Your Customer (KYC) automation and review incoming information for sentiment analysis.  

Sales

AI agents may facilitate sales in many ways. Here are several examples:

 

• AI agents can identify prospects and ICPs and qualify leads.
• AI chatbots can respond to customer inquiries and, recognizing their intentions and emotions, intelligently guide conversations toward securing sales. 
• They can also use historic and real-time customer behavior data to recommend new products and upgrades.
• AI agent integration with CRMs and other sales software helps automate the sales funnel.
• After a purchase, AI chatbots can help solve issues and provide information. 

 

Marketing

Automation, generative, and analytic abilities make AI agents a valuable tool for marketers. For example:

 

• AI agents can analyze social media and reviews to understand public sentiment, preferences, and trends to inform marketing decisions.
• Data agents combing through sales data can uncover insights crucial for personalization, more effective product listings, and more accurate targeting.
• Marketing departments can utilize tools like Reword’s AgentGPT to create initial drafts for ads, blog posts, social media content, product listings, reports, etc.
• AI agents can also monitor ad/campaign performance and optimize them automatically.

AI Agents as Virtual Assistants

AI agents with text and voice user interfaces can answer queries, manage personal schedules, send emails, plan activities, set appointments, set reminders, and tackle other daily tasks.

 

They also drive the shift from linear task execution to iterative workflows with multiple stages of thinking, researching, and revising, leading to significantly better results. 

Here are several processes most likely to benefit from the integration of an AI agent:

 

• Communications. Onix’s social network auto-reply assistant is an example. NLP and deep learning enable it to answer messages and keep chatting on social networks when the user is offline. Moreover, as the assistant learns from conversations with specific contacts, the communication becomes increasingly personalized.

 

• Document management. AI agents automate document sorting and archiving and generate various business document drafts.

 

• Human resources. AI agents streamline recruitment and onboarding by generating job posts, screening resumes, scheduling interviews, and explaining company policies, benefits, etc.

 

• Project management. AI agents can allocate tasks considering team members’ skills and availability, automate progress tracking, update PMs and team members in real time, and provide predictive analytics for proactive adjustments.

 

• Travel planning. AI agents can answer travel-related questions, search for deals, book flights and hotels, and even create itineraries to match personal preferences.

 

Finance

A chatbot embedded in a credit card portal may ask a customer about their needs or issues and, using their answers and information from the customer profile, documents, bank policies, etc., suggest a course of action or pass them on to a specialist.

 

Read also: Build Your Neobank in 2025: Onix Step-by-Step Guide

 

AI agents acting as financial advisors can analyze market trends and consider customer preferences to recommend assets for investment portfolios or even manage these portfolios and protect them against market fluctuations autonomously.

Intelligent agents can also streamline loan applications, automate credit risk assessment, and continuously monitor existing customers for credit risk.

 

One of the best use cases of AI agents in finance is fraud detection and prevention. These agents can study cybercrime patterns and monitor user activities and transactions in real time to

 

• uncover abnormalities
• alert customers about suspicious activities
• flag suspicious transactions and terminate them automatically
• detect fraudulent behavior and alert the bank

 

Learn more: How AI is Reshaping the Banking Industry: Benefits & Use Cases

 

AI agents can also scan financial software for security vulnerabilities and report them before damage occurs.

 

Learn more: Fintech App Security Best Practices: Stay Safe

 

Financial institutions can also have AI agents process invoices, organize records, transform raw data into charts and graphs, create visuals for reports, visualize consumer trends and campaign performances, predict future budget needs, and perform other back-office tasks.

Retail and eCommerce

For online marketplaces and stores, AI agents can perform a variety of functions:

 

• power recommendation engines
• help customers find what they need and answer product questions
• recommend items based on purchase history
• handle communication on orders
• manage returns, exchanges, and refunds
• detect fraudulent activities
• manage inventory
• integrate and analyze data across the supply chain to facilitate more accurate forecasting of supply chain needs

… and perform other tasks that help streamline sales and personalize shopping experiences.

Read also: Building a Grocery App in 2024 – Tips from Onix

 

Education

AI agent development in the education and learning contexts promises more personalized, engaging, efficient, and accessible learning experiences. Some of the use cases include, but are not limited to:

 

• The analysis of student performance, learning styles, and feedback will facilitate the creation of customized learning plans, exercises, resources, etc.
  AI tutors may answer students’ questions, assess tests, identify problems, and provide personalized guidance and assistance anywhere 24/7.
• AI agents can grade assignments, quizzes, and exams quickly, accurately, and objectively, generate detailed feedback, specify mistakes, recommend resources, etc.
• Data agents analyzing student performance, engagement, and progress will provide actionable insights, identify trends, and help build strategies to improve learning outcomes.
• AI agents running virtual classrooms and labs and simulating specific environments and scenarios will facilitate remote learning, safe training, and interactive content delivery. 

Learn more: The Guide to Create Your VR Training Simulator

 

Healthcare

Some use cases of AI agents in the healthcare sector include:

 

• An AI-powered portal can inquire patients about their symptoms or needs and automatically schedule relevant doctor appointments, direct them for a lab test or scan, send appointment reminders, offer educational materials, etc.
• AI agents can analyze the lab test results and images from ultrasounds or scans to find the signs of specific diseases, summarize the findings, suggest diagnoses, recommend therapies and medications, and submit these to the assigned doctor for review.
• Intelligent agents can analyze patients’ symptoms, medical history, and hereditary diseases to provide some initial insight or possible diagnoses to doctors.
• AI agents can also speed up issuing repeat prescriptions by collecting information during a call with a patient, sending it to a doctor to check the correctness of the prescriptions, and sending the approved prescription to the patient.

  
AI agents can also optimize hospital workflows by predicting patient admissions, managing doctors’ schedules, and improving resource allocation.

Transportation and logistics

Autonomous vehicles remain the most impressive application of AI agents in the business. For instance, the world’s first autonomous ride-hailing service Waymo already provides tens of thousands rides per week in Phoenix, San Francisco, and LA. With current statistics like

 

• 48% fewer police-reported crashes
• 73% fewer injury-causing crashes
• 84% fewer airbag deployment crashes
• 76% fewer property damage claims
• 100% fewer bodily injury claims than human-driven cars,

 

Waymo’s claim to be the most trusted driver seems justified.

 

AI agents can

 

• plan optimal routes considering delivery schedules, traffic data, road conditions, weather, and vehicle performance
• track shipment and respond to changes in real time by redirecting vehicles to less congested routes and adjusting delivery schedules
• monitor vehicles and alert managers of any technical issues

 

Manufacturing

Artificial intelligence agents can take over dangerous jobs or tasks that require speed or accuracy beyond human capacity. For instance:

 

• Intelligent agents can schedule production processes and optimize resource usage.
• AI agents can use computer vision to analyze data about manufacturing processes to identify and report hazardous conditions or safety protocol breaches. Unlike regular AI models that only detect anomalies, AI agents can actually activate an alarm system, stop a conveyor belt, or adjust device parameters. 
• AI agents can spot even microscopic product defects by analyzing sensor and visual data.
• Intelligent agents can continuously track equipment sensor data and performance metrics to detect problems with equipment and production processes early on.
• AI agents incorporated in robots can tackle repetitive tasks, operate in dangerous environments, and perform precision work.

 

For example, Boston Dynamics’ Spot makes processes and environments safer for employees.

 

 

Read also: 5 Technology Trends in the Construction Industry in 2025

 

Software development

Code agents and other types of AI agents help software engineers and improve development and maintenance workflows by automating repetitive processes, suggesting code improvements, tracking down issues by querying log data, debugging, troubleshooting, KPI tracking, and so forth. 

For instance, ChatDev AI’s agents can take on the roles of programmers, testers, and documenters and collaborate on software development and deployment.

 

Some Benefits of AI Agent Implementation

Businesses are increasingly leveraging the benefits of AI agents to improve customer and employee experiences, processes, products, and bottom lines. 

If you make AI agents a part of your organization and infrastructure, you may expect the following advantages and improvements:

 

Higher efficiency and productivity

By taking over routine tasks and solving problems without human intervention, artificial intelligence agents reduce the workload on employees and teams, saving their time and energy for more meaningful work.

 

Happier employees make a more efficient and productive workforce. Custom task-based AI agents can also perform certain tasks faster and better than humans. 

 

For example, the integration of an AI agent allowed the freelance labor marketplace TaskRabbit to manage a dramatic increase in demand after expansion. They needed a solution that would also route queries to the right countries by picking up on location and recognizing regional dialects like Canadian French or US Spanish. 

TaskRabbit saw near-instant ROI from AI agent implementation. Now, 100% of the first chats are automated, and the 28% deflection rate far exceeds their initial KPIs. Their support agents have also reported increased job satisfaction.

 

Scalability

AI agents efficiently scale operations and ensure service consistency across growing businesses without the need for proportional increases in human resources.

 

Higher customer satisfaction and loyalty

AI agents that process and analyze data like customer conversations, purchase history, preferences, reviews, social media, etc., will drive the increasing personalization of interactions, ads, offers, and services and allow companies to address customer needs proactively.

Read also: Hyper-Personalization in Hospitality: Reaching New Heights

AI agents that provide support and resolve issues 24/7 regardless of a customer’s location and time zone and understand customers’ sentiments help build trust and loyalty.

 

According to Zendesk’s Customer Experience Trends Report 2024, 69% of organizations believe that generative AI can help humanize digital interactions. 53% of consumers report that they would prefer interacting with AI agents because they don’t make errors.

 

Better strategic planning and decision-making

These benefits of AI agents implementation are clear in Onix’s recent project – an expert system that uses algorithms and geospatial tools to assess land development feasibility.

 

The system divides a terrain with a grid, calculates gradients, and identifies flat areas optimal for construction. It also evaluates sites’ connectivity by measuring the distance to roads and utilities.

 

Risk mitigation

AI is invaluable when it comes to risk assessment and management. AI agents that accurately detect fraud and security breaches in real time prevent criminal activities and enable organizations to avoid the loss of assets, reputation, and customer trust, as well as litigation and penalties. Task automation also minimizes costly errors.

 

Competitive advantage

Organizations and entrepreneurs leveraging AI agents in the business planning, product development, marketing, and customer service departments are likely to stay ahead of the competition.

 

Revenue increase

According to McKinsey’s 2024 survey, significant revenue increases (5% and more) are most often seen from analytical AI adoption in marketing and sales (reported by 71% of respondents), human resources (65%), and supply chain and inventory management (63%).

 

Generative AI seems to have the best effect when applied in risk, legal, and compliance management (as reported by 62% of respondents), IT (56%), and marketing/sales and supply chain management (53% of respondents).

 

Cost reduction

AI agents can handle multiple processes simultaneously and don’t need breaks. At production facilities, AI-powered predictive maintenance facilitates saving on repair costs and downtime.

According to the same McKinsey’s survey, meaningful cost reductions from generative AI are most often seen in HR (half of the respondents), supply chain and inventory management (46%), and service operations (45%).

 

Analytical AI most effectively reduces costs in service operations (49% of respondents), supply chain and inventory management (43%), and software development (41%).

Source

 

Along with the benefits, organizations that consider various types of AI agents should be aware of their limitations, possible risks, and hurdles to implementation.

 

Some Limitations and Challenges of AI Agent Implementation

AI agents’ key limitations are accuracy and predictability.

 

Learn more: What Are the Risks and Limitations of Machine Learning?

 

For example, it’s possible to build a basic AI teaching assistant based on a GPT interface in several weeks. However, it will produce results with only 60% accuracy, and it will take at least ten months to raise the accuracy level to 90%.

 

A generative AI agent can also hallucinate, producing plausible responses that are not factually correct. One way to mitigate this risk, e.g., in a bank chatbot, is to integrate fact checking for every AI-generated text: if it proves accurate, it’s released to the customer; if not, the chatbot puts them through to a human operator. 

Right now, not a single publicly available monolithic LLM is capable of handling genuinely agentic tasks with multi-step reasoning. It will likely take multiple models to do it.

 

In their current state, AI agents are best suited for applications that allow slight output variations. Otherwise, a hybrid approach including human verification is recommended.

 

The challenges of implementing AI in business settings require evaluating a business’s AI readiness, preparing data, selecting an appropriate platform, and researching data privacy and security requirements.

 

 

Entities that deploy AI agents in controlled environments see promising results, but scaling these operations may be challenging. Using AI at scale calls for rewiring some organizational functions, implementing data management frameworks, cleaning the data, ensuring it is compliant and free of bias, etc.

 

AI agent development can require significant investment in software, hardware/cloud, hardware, network systems capable of supporting AI, data management, and workforce.

 

Learn more: How to Build Machine Learning Teams for AI Projects

 

The integration of AI agents with existing business workflows can be complicated if you use legacy systems lacking basic API access.

 

For AI agents to operate and handle contingencies in a truly autonomous way, organizations and industries must create a tech stack designed specifically for AI agent development and infrastructure to support AI agents and applications that rely upon them.

You will need to continuously check and improve your AI agents’ compliance with industry-specific regulations and standards. This is particularly important in healthcare, finance, and legal services.

 

Read also: A Guide to HIPAA Compliance for Software Developers

 

AI agents also raise ethical concerns about fairness, transparency, accountability, and risks associated with bias, discrimination, and privacy infringement. AI may know better how to perform critical tasks in practice but can be dangerous when it comes to knowing what to do in contexts that can change in ways that an agent’s training data did not address properly.

How to Implement AI Agents

Entrepreneurs have two options:

 

1. Implement an existing solution
2. Build a custom AI agent

 

Entrepreneurs can opt for off-the-shelf AI solutions that can be deployed in minutes. For example, Zendesk’s proprietary AI models were purpose-built for accurate, personalized support and don’t require any coding or engineering help.

You may also customize available tools to meet your specific business needs.

 

If you choose to build an AI agent specifically for your company, some strategic advantages of custom AI agent development for a business may include:

 

• tailoring the AI agent’s functionalities and interactions precisely to your business processes and needs 
• no need to pay for unnecessary features of a generic solution
• seamless integration into your existing workflows and infrastructure
• a chance to innovate and build unique features that will give you a competitive edge in today's technology- and data-driven world
• independence and full control over the system, allowing you to improve your AI agent’s functions, operation, and performance
• the ability to scale, adapt, and upgrade the system as necessary to support the business’s long-term growth and success

Building a custom AI agent requires time, effort, and technical knowledge. AI agents must be designed with robust security measures and compliance protocols in mind. An experienced AI/ML team’s expertise will prove invaluable.

Although the initial investment in the development and integration of an AI agent may be higher than off-the-shelf software costs, the advantages of a custom solution will maximize ROI over time.

All types of agents in artificial intelligence can be built following this high-level scenario:

 

1. defining the objectives and tasks your AI agent will perform
2. data preparation and integration
3. selection of an appropriate AI agent platform
4. design of the AI agent architecture
5. development and testing
6. integration and deployment 
7. evaluation and fine-tuning

 

Learn more: A Business's Step-by-Step Guide to an Artificial Intelligence Project

Depending on your agent’s intended functionality, you will have to collect large volumes of text, images, audio, or other forms of input relevant to your business. This step will likely involve data cleaning, resolving inconsistencies, and bringing data from various sources into a unified format.

Your objectives will also determine the choice of tools and technologies, such as ML algorithms, NLP frameworks, computer vision techniques, etc.

There are two types of AI agent development platforms:
 

1. no-code AI agent builders like the web-based Flowise and MultiOn
2. code platforms, such as AutoGen, AutoGPT, and Crew AI 

The factors to consider include, but are not limited to, a platform’s capabilities, compatibility with your existing systems and user interfaces, scalability potential, and support availability. The decision may require thorough research and trials.

When developing the architecture for your AI agent, the AI team must consider modularity, the ability to accommodate growing data volumes, user demands, and evolving use cases, integrations with external services and APIs, and performance requirements. It must comply with all applicable regulatory requirements and provide for all necessary security measures.

Learn more: How to Build a Machine Learning Model for Your Business

Next, the AI dev team implements the selected algorithms and functionalities, iteratively testing and refining the AI agent's performance. They train the agent’s model on company-related data, fill its memory with the latest domain information, continuously evaluate the agent’s accuracy, and make necessary adjustments to improve your AI agent’s effectiveness.

They may not necessarily need to train your model from scratch, but even existing pre-trained models may require fine-tuning to adapt to your business context or to be supplemented with this context using techniques like Retrieval Augmented Generation.

Finally, the team integrates the AI agent into the target environment or application and deploys it for real-world usage. Beta testing with users and feedback analysis can help identify problems and areas for improvement.

AI development is an ongoing process of learning, adaptation, refinement, and optimization. The AI agent owner must persistently collect user feedback, monitor usage patterns, and update it with new data to keep it effective, relevant, and competitive. Consistent evaluation of the agent’s performance and assessment of its business value is crucial for success.

 

Final Thoughts

AI agents already augment human resources by taking over routine tasks in customer service, marketing, sales, and supply chain management. As the underlying technology advances, we will soon see increasing autonomy and more use cases of AI agents across various sectors.

 

The best place for initial AI agent implementation is repetitive task automation. From there, organizations can move on to empowering their employees or customers for supernatural productivity, creativity, insights, and other ways to add value and improve their lives.

It’s also essential to stay abreast of the latest developments in AI and continuously monitor and improve your existing AI agent’s performance for better outcomes and competitive advantage.

Entrepreneurs and organizations opting for AI need reliable technology partners. Onix’s interdisciplinary team has experience in ML, LLMs, generative AI development, and data strategy and management. We provide the following services:

 

• strategic consultations to clarify the potential of AI agent implementation for your business, integration opportunities, and possible project budget
• proof of concept service to help you speed-test and validate your AI agent concept  
• AI agent development on publicly available platforms or building a custom AI agent tailored to your business goals and processes
• AI agent integration into your technology ecosystem for smooth interoperability and minimal disruption
• support, monitoring, and optimization to help your AI agent stay ahead of market trends

 

Please don’t hesitate to contact us if you have any questions, look for professional services, or need help with your AI and ML solution implementation.

 

FAQ

 

What is the difference between an AI agent and a chatbot?

AI agents represent a broader category with more applications and capabilities beyond conversational interactions. Unlike chatbots and robotic process automation software, AI agents are rational.

 

Their reaction to the same situation will depend on the input and the environment. Autonomy means that users needn’t constantly send prompts to make AI agents do their job.

 

What are the advantages of AI agents over traditional automation systems?

AI agents are distinguished by their higher autonomy, adaptability, and intelligence. Their capability for understanding context, making decisions, and predicting outcomes makes AI agents suitable for more complex tasks in dynamic environments.

 

Can AI agents replace human support agents?

Yes and no, depending on the task. AI agents will likely replace humans at tedious jobs like sending generic emails or answering frequently asked questions on live chats or over the phone. However, more complex issues and activities that build customer relationships will likely remain human agents’ responsibility.

 

Can companies create their own AI agents?

Yes, AI agent builders enable businesses to make AI agents quickly and cost-effectively.

 

How long will it take to implement an AI agent?

Our developers can build an AI agent beta version operating in a controlled environment in several months. Testing and experimentation to achieve the desired level of accuracy and stability may take up to 10 months more. 

 

How do AI agents ensure the security of customer data?

AI agents that handle sensitive data can protect it by anonymizing or pseudonymizing it wherever possible and restricting access to sensitive data to authorized entities.

 

How to integrate AI agents into business processes?

A common way to integrate AI agents into existing company systems is through APIs. They enable intelligent agents to communicate with other software used by a business.