What is an AI Agent?
Mayank Patel
Dec 23, 2024
5 min read
Last updated Dec 23, 2024
AI agents are a revolutionary force for new businesses. They can take care of tasks like customer support, data hunting, resource management, and more—helping you make quicker and smarter decisions. With AI at the helm, you can leave the busywork behind and dive headfirst into building your vision.
To understand what an agent is in AI, we need to look at its key features. An agent is a system that:
- Perceives: It gathers data from its environment through inputs like APIs, user interactions, or system logs. This helps the software understand its context and surroundings.
- Decides: An agent uses algorithms and pre-trained models to evaluate options based on goals, preferences, and conditions. This allows them to act intelligently instead of just following preset scripts.
- Actions: These are the actions performed by an AI agent as output, based on the information it processes. They can range from simple responses to controlling machinery or managing workflows.
Perception, action, and decision-making form the core of intelligent behavior in an AI agent. They may be autonomous or semi-autonomous agents, revising strategies according to changes either in the circumstances or new information.
Here are some common classifications:
- Reactive Agents: This agent reacts to specific triggers based on predefined rules, without keeping track of its internal state. It's often used in simple applications like basic chatbots or automated response systems.
- Deliberative Agents: These agents are the opposite of simple reactive ones. They maintain an internal model of the environment and use past experience to plan actions.
- Learning Agents: These agents use machine learning to improve over time. They can adjust their behavior based on feedback or new data, making them ideal for dynamic environments.
- Hybrid Agents: These agents combine features of both reactive and deliberative agents, making them versatile and efficient at handling various tasks. They use the strengths of both approaches to achieve optimal results in different situations.
- Intelligent Agents: This term is often used to refer to “AI agents.” Intelligent agents have advanced reasoning skills that allow them to solve complex problems.
Also Read: AI in Supply Chain: Use Cases and Applications With Examples
An AI agent can be designed to work in any environment and serve many use cases. Here are some of them:
Customer Support
These agents can provide instant responses through chat, email, or voice. For example, they can help resolve common issues like password resets, account inquiries, or order status updates. AI-powered agents can also engage in more advanced interactions like troubleshooting technical issues or handling complex support requests by escalating them to human agents when necessary. These agents can be used in industries like e-commerce, banking, telecommunications, and healthcare, ensuring 24/7 availability and reducing wait times.
Finance (Fraud Detection)
AI agents in finance can be used for identifying and mitigating fraudulent activities in real-time. These agents analyze transaction patterns and flag suspicious activities that deviate from typical behavior. They use techniques like anomaly detection, predictive modeling, and pattern recognition to detect fraud in various contexts, such as credit card transactions, insurance claims, or wire transfers. These AI systems can also help assess risks by evaluating credit scores, past transaction history, and demographic data. Financial institutions, e-commerce platforms, and payment processors can deploy these agents to improve security.
Data Analysis
AI-powered data analysis agents sift through vast amounts of structured and unstructured data, uncovering trends, patterns, and correlations that would be time-consuming and challenging for people to detect. These agents can generate actionable insights from historical data, predict future outcomes, and offer real-time decision support. For example, an AI agent can analyze customer behavior data to predict churn rates or sales trends. In the healthcare sector, AI agents can analyze patient data to predict disease outbreaks or assess treatment efficacy.
E-commerce (Product Recommendations)
These agents analyze data such as past purchases, cart abandonment, and product ratings to suggest items that a customer is most likely to buy. For instance, an AI agent can recommend complementary products or upsell higher-value items by predicting what the shopper might need next. In addition to individual recommendations, AI agents can optimize entire product catalogs based on trends and customer preferences, helping e-commerce businesses drive higher sales and customer satisfaction.
Manufacturing (Predictive Maintenance)
In manufacturing, AI agents focus on monitoring the health of machinery and equipment, predicting when maintenance is needed before a breakdown occurs. These agents collect data from sensors on machinery, analyze usage patterns, and identify wear and tear that might lead to failure. By anticipating maintenance needs, these agents help reduce downtime, extend the lifespan of equipment, and optimize production schedules. AI agents can also prioritize which machines need attention based on the criticality of their failure to the production line. This technology is applied in industries like automotive, electronics, and energy production.
Legal (Document Review)
AI agents in the legal sector assist with document analysis, reviewing contracts, legal briefs, and case files to identify key clauses, terms, and potential risks. These agents use natural language processing (NLP) to understand legal language and flag issues such as missing terms, inconsistencies, or non-compliance with regulations. For example, an AI agent can help lawyers review hundreds of contracts in a fraction of the time it would take manually. Legal AI agents are also used for e-discovery, helping lawyers find relevant documents for litigation or investigations.
HR (Candidate Screening)
AI agents in human resources streamline the recruitment process by automating candidate screening and assessment. These agents analyze resumes, cover letters, and interview responses, identifying candidates who meet the job requirements and company culture. They can assess qualities like technical skills, work experience, and even soft skills by analyzing text and video responses.
Retail (Inventory Management)
AI agents in retail manage stock levels, predict demand, and optimize supply chains by analyzing sales patterns, seasonal trends, and market conditions. These agents help retailers avoid stockouts and overstocking, ensuring that the right products are available at the right time. For example, an AI agent can predict that certain products will sell faster during holidays and ensure that inventory levels are adjusted accordingly. These agents can also automate reordering processes, ensuring efficient use of resources and reducing waste.
Conclusion:
Understanding what an AI agent is-and how it works-can open all sorts of new opportunities for you. At Linearloop, we specialize in crafting state-of-the-art solutions that use AI to simplify workflows.
Be it automating customer support or creating personalized experiences for your users, we shall walk you through it all in effective solution building, available for your specific needs.
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Who are AI Agencies?
Feeling lost in a world of AI? You are not alone. Many businesses understand the importance of AI but find it really difficult to understand how to use it. From machine learning to natural language processing, AI is pretty complex. That's where the AI agencies come in.
Work with AI agencies now because AI drives change at an exponential pace. Experts give you tools you might lack in-house. This helps you work smarter, solve problems faster, and understand your customers better. AI agencies help you board these changes far quicker than your competition. You don’t have to do it alone. But let’s start with the basics first.
AI agencies build solutions that can "learn" and make decisions based on data, often automating tasks and improving over time. Traditional agencies typically focus on rule based or static solutions with limited ability to adapt, learn, or improve over time. Here's how a typical project might differ when built by a traditional software agency versus an AI agency:
Example 1: Chatbot for Customer Support
Traditional Software Agency:
A traditional agency would likely build a chatbot using pre-configured templates. The bot would follow a script with predefined responses for specific keywords or actions.
vs
AI Agency:
An AI agency would develop an intelligent, machine-learning-powered chatbot capable of understanding and responding to customer queries dynamically. The chatbot could read and learn user interactions and improve its responses over time based on data.
Key Features:
- Natural language processing (NLP) for understanding context,
- Ability to learn from past interactions, and
- Handle complex queries
Example 2: E-commerce Recommendation System
Traditional Software Agency:
A traditional agency might build a simple recommendation engine based on predefined rules like "customers who bought X also bought Y." This system uses static rules for recommending products, which can be limited and often doesn't evolve or adapt to new data.
vs
AI Agency:
An AI agency would use machine learning algorithms like collaborative filtering or deep learning to create a dynamic recommendation system that personalizes product suggestions based on each customer's behavior, preferences, and past interactions.
Key Features:
- Adaptive recommendations that improve over time,
- Can analyze large datasets for more accurate predictions, and
- Personalized to each visitor or customer
Example 3: Image Recognition for Quality Control
Traditional Software Agency:
A traditional agency might build an image recognition system using basic image processing techniques like edge detection or template matching to identify defects in products.
vs
AI Agency:
An AI agency would build a computer vision system using deep learning algorithms like convolutional neural networks (CNNs) to identify product defects in images. The system could be trained to recognize defects from various angles, lighting conditions, and types of products.
Key Features:
- High accuracy in complex environments,
- Ability to improve through continuous learning, and
- Detects a wide range of defects across different contexts.
Here’s how these new AI agencies can serve you:
Custom AI Models
These new agencies build new learning models just for your organization. That means using your private data to make sure the model is built on accurate and relevant parameters.
Analyzing “Chunks” of Data
At the centre of every successful AI initiative is data. AI agencies have the expertise and experience in conducting extensive data analysis. They help you make the most of your data.
Also Read: Overcoming AI Implementation Hurdles: Why Simple API Calls Fall Short in Conversational AI
Building Tailored AI Agents
AI agencies can build specific action-driven AI agents. These agents can improve efficiency and responsiveness whilst helping to alleviate human staff burdens by acting autonomously.
Integrating AI into business has its own set of unique challenges. Herein are some of the top reasons why one needs to partner with an AI consulting agency ASAP:
Closing the Skill Gap in AI
Many organizations lack the in-house expertise to execute a successful AI strategy. AI agencies fill this gap by offering skilled professionals who understand the nuances of AI and can guide businesses through the implementation process
Delivering Custom Solutions to Unique Problems
Every business faces unique challenges, and there’s no one-size-fits-all solution. An AI agency collaborates closely with clients to create strategies tailored to their specific needs and goals.
Also Read: AI Software Development: Key opportunities + challenges
Reducing the time taken to bring AI Applications into Market
Deploying AI is time-consuming and resource-intensive. Partnering with an AI agency helps you speed up implementation and capitalize on emerging opportunities much faster.
AI agencies can be confusing—what do they do, how do they work, and how to choose the right one. At their core, they are specialized teams that help you unlock value with AI, from strategy to implementation—but it’s not always as simple as it sounds.
This is where Linearloop.io comes in: we simplify AI for you, and help you understand their role in your business pipeline. We develop the right-fit AI strategy and models that integrate seamlessly with your existing tech stack.
Mayank Patel
Jan 16, 20256 min read
How to build AI agents with Ruby
Ruby offers an excellent launch pad to develop AI agents. Known for its simplicity and developer-friendly syntax, Ruby enables you to create highly sophisticated AI agents without the burden of overly complex code.
AI agents come in various forms, each with different levels of sophistication and decision-making capabilities. Some common types include:
- Reactive Agents
- Learning Agents
- Utility-Based Agents
AI agents are finding diverse applications across various industries:
- E-commerce: Personalized recommendation systems, chatbots for customer service.
- Healthcare: Diagnostic tools, robotic surgery assistants.
- Finance: Fraud detection, algorithmic trading.
- Logistics: Optimized routing for delivery vehicles, warehouse automation.
While often associated with web development, Ruby offers compelling advantages for AI agent development, especially for beginners:
- Simplicity and readability for AI beginners: The syntax of Ruby is clean and intuitive, which really lowers the barrier to entry. You can understand the core logic of your code without getting lost.
- Libraries and “gems” available for AI and machine learning in Ruby: The Ruby AI ecosystem is smaller compared to Python's but offers some powerful “gems” for AI and machine learning tasks:
NOTE: In Ruby, gems are like small add-ons or tools that you can plug into your code.
- Numo::NArray supports high-performance numerical computations, essential for data processing in AI applications.
- SciRuby provides tools for scientific computing and data analysis, enabling effective handling and interpretation of datasets.
- While Ruby lacks native machine learning libraries, you can use TensorFlow.rb as a Ruby binding to leverage the TensorFlow framework's advanced machine learning capabilities within your Ruby projects.
- Community support and resources: Ruby has quite an active community, and those who are well-known in that community are truly very helpful. Numerous online forums and tutorials, documentation—everything to help you—is available for development needs related to an AI agent.
Before you start building your AI agents, you'll need to set up your development environment. Here are the essential tools and libraries:
- RubyInstaller (or your system's Ruby package manager): This will install the Ruby programming language on your machine.
- Numo::NArray: A powerful gem for numerical operations. Install with the command: gem install numo-narray
- Sciruby: A gem for scientific computing. Install with: gem install sciruby
Setting up your development environment means installing Ruby, then using RubyGems to install the needed libraries. A very simple way to check if everything is installed properly is to open your terminal or command prompt and type ruby -v to check the version of Ruby installed.
Building an AI agent, regardless of the language, generally follows a structured process:
Step 1: Define the Problem and Agent’s Goal
The first important step is to define the problem domain in which your AI agent will work. What precisely will it do? For example, will it be an agent to recommend interesting articles to the users or just a simple agent to automate some repetitive task? Having defined the problem, you need to define the objectives of the agent. What precisely are the goals that the agent should accomplish in that domain? Goals should be measurable and clearly define what a success for the agent is.
Step 2: Design the Agent’s Architecture
An important decision you have to make here is whether you take a rule-based approach or a learning-based approach. Rule-based agents follow a set of predefined rules to make a decision. They excel in problems for which the decision logic is well-defined and very clear. On the other hand, learning-based agents learn from data in making decisions. This approach is better for more complex problems in which the rules are not very clear. You will also want to pay attention to the structure of the inputs of the agent, how it gathers information, the outputs, the actions it takes, and the decision processes that connect them.
Step 3: Develop the Agent
This is where you would begin to implement the core logic for your AI agent in Ruby. It's far easier to do so since Ruby syntax is clear. You will call upon the installed libraries to handle either the data processing or the implementation of machine learning algorithms—provided you have chosen a learning-based approach—or define the rules for your rule-based agent. Numo::NArray can be used in manipulating numerical data, while TensorFlow.rb may be used to build and train a neural network.
Step 4: Train and Test the Agent
This means feeding your learning-based agent a lot of training data. The data will help the agent learn patterns and fine-tune its algorithms. After training—be it whatever type of agent—testing becomes an important step. You need to test how the agent performs under various scenarios, ensuring it behaves in a manner expected of it; that its goals as set are achieved.
Building AI agents with Ruby, like any development endeavor, comes with its own set of challenges:
- Performance limitations: While Ruby is excellent for readability and rapid development, it might not be the fastest language for computationally intensive machine learning tasks compared to languages like Python or C++.
- Workaround: For demanding tasks, consider leveraging the power of libraries like TensorFlow.rb, which provides optimized backend execution.
- Smaller ecosystem compared to Python: The Ruby ecosystem for cutting-edge AI research and specialized libraries might be smaller than Python's.
- Workaround: Focus on leveraging the strengths of existing Ruby libraries and consider integrating with external services or APIs if needed.
- Complexity of certain AI concepts: Understanding the underlying mathematical and statistical concepts behind certain AI algorithms can be challenging for beginners.
- Workaround: Start with simpler agent types and gradually delve into more complex concepts as your understanding grows. Utilize the abundant online resources and tutorials available.
Conclusion
Building AI agents can be intimidating at first, but Ruby makes it a pretty approachable goal with its friendly syntax and burgeoning ecosystem of powerful libraries. The future of intelligent solutions is in your hands, and you will be able to embark confidently into this new exciting era equipped with the right approach and an ideal partner in your endeavor.
Whether it is tapping into the potential of AI agents for business process simplification or developing next-generation applications, Linearloop is there to help you move forward. Discover how our software development expertise and emerging technologies can accelerate your AI initiatives and guide you through the exciting landscape of intelligent automation with confidence.
Mayank Patel
Jan 8, 20255 min read
