Mayank Patel
Jan 8, 2025
5 min read
Last updated Jan 8, 2025
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:
AI agents are finding diverse applications across various industries:
While often associated with web development, Ruby offers compelling advantages for AI agent development, especially for beginners:
NOTE: In Ruby, gems are like small add-ons or tools that you can plug into your code.
Before you start building your AI agents, you'll need to set up your development environment. Here are the essential tools and libraries:
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:
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.
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.
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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
Here’s how these new AI agencies can serve you:
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.
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
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:
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
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
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
What is an AI Agent?
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:
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:
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
Mayank Patel
Dec 23, 20245 min read
