How to Create Custom AI Agents for
E-Commerce Growth in 2025

Why E-commerce Businesses Need AI Agents

The expectations of e-commerce customers are changing fast. They look for more personalised and seamless shopping experiences. But how can e-commerce businesses give them what they look for? AI agents are exactly what they need.

The AI agents help e-commerce businesses improve customer experiences, simplify operations, and boost sales. Here is how AI agents are reshaping the e-commerce landscape.

• Effortless Product Discovery : AI agents make it easier for customers to find the right products. They understand search intent and preferences, even when the customer’s query is vague.
• Shopping Assistance : AI sales agents can act as virtual shopping assistants and guide customers through their buying journey. They answer product questions, compare options, and offer recommendations based on customer behaviour. For instance, when someone abandons their conversation, the AI can send a reminder or a personalised offer to bring them back.
• Personalised Recommendations :AI agents analyse browsing history, past purchases, and real-time behaviour to deliver hyper-personalised recommendations. They can predict what customers are most likely to buy next, improving cross-selling and upselling opportunities.
• More Efficient Customer Support : AI support agents can handle customer inquiries 24/7 without human intervention. They instantly resolve common issues like order tracking and FAQs, reducing the support team workload. They can also autonomously raise tickets when handling issues that need deeper consideration and can also escalate complex issues to human agents, ensuring a seamless support experience.

2 Ways E-Commerce Businesses Can Create Custom AI Agents

There are 2 custom AI agent development methods available for e-commerce businesses.

• Using A No-Code Custom AI Agent Builder
• Building Custom AI Agents From Scratch

1. TechMonk: No-Code Custom AI Agent Builder

TechMonk makes building custom AI agents easy with its no-code AI agent builder. E-commerce businesses can create and customise AI agents without needing any technical expertise. The setup is simple and fast, making AI agents more accessible than ever. They only have to answer a few questions during setup, and the AI agents are ready to go and have access to prebuilt full-stack marketing tools available in the tool library which can be mapped to them to enhance their capabilities.

TechMonk’s agentic workflows let businesses assign custom AI agents to different roles throughout the customer journey. Whether it is guiding a sale and handling support or beyond, these workflows ensure that AI agents manage each task efficiently. This creates seamless campaigns and consistent customer interactions.

Building Custom AI Agents On TechMonk

E-commerce businesses can now create AI agents tailored to their unique needs with no coding required. The AI Agent Builder by TechMonk is designed to make the process simple and accessible. Businesses can set up custom AI agents without any technical expertise.

The setup is easy. Businesses just need to answer a few key questions to configure AI agents that match their specific needs. Whether it’s automating tasks, improving customer engagement, or personalizing experiences, the AI Agent Builder makes it possible to design AI agents that fit perfectly into daily operations.

With its user-friendly interface, the AI Agent Builder helps businesses quickly create and launch AI agents that enhance efficiency and customer interactions. It removes the complexity of development, giving businesses full control of their AI-powered solutions without the need for coding or technical skills.

On top of that, TechMonk’s AI agentic platform comes with two prebuilt AI agents.

AI Sales Agents

TechMonk’s AI Sales Agents help e-commerce businesses automate their sales process. It engages potential customers, understands their preferences, and guides them toward making a purchase. By automating repetitive tasks, it helps businesses increase conversions and improve efficiency.

• Effortless Product Discovery : TechMonk’s AI Sales Agent makes product discovery easy by suggesting options based on customer needs and preferences. It also compares similar products, helping customers quickly find the best match.
• Upsell Products : The AI agent for sales identifies upsell opportunities by recommending complementary products to customers. The recommendations are based on the customers browsing history and past purchases.
• Drive Sales Growth :The AI agent provides personalised discounts and assists customers during checkout. By reducing drop-offs and cart abandonment, it helps businesses close more sales every day.

AI Support Agents

TechMonk’s AI Support Agents manages customer queries, order tracking, returns, and issue resolution around the clock. It helps reduce response times and improves customer satisfaction by providing quick and accurate answers.

What happens when the issue is too complex for AI to handle?

The AI agent for customer support automatically creates a support ticket and gives customers a resolution timeline (TAT). This keeps customers informed and ensures they know what to expect next.

It also understands customer emotions. If it detects frustration or negative sentiment, it escalates the conversation to a human agent. This support ensures that even unhappy customers receive personalised help.

2. Build Custom AI Agent From Scratch

Define Use Cases

Before developing an AI agent, it is important for the e-commerce business to be clear about its purpose. Is it for customer support, sales automation, or product recommendations?

Understanding the problem the AI needs to solve is necessary for designing the right capabilities and conversation flows. Should the AI function as an AI chatbot, voice assistant, or recommendation engine? Deciding this upfront ensures the AI is built with the right features and aligns with business goals.

AI agents need high-quality data to perform well. Businesses must collect customer interactions, purchase history, browsing behaviour, and support tickets to train the models.

Data Collection & Training

How can businesses ensure the data is reliable? It needs to be cleaned and structured properly to avoid bias and errors. Machine learning techniques then help train the AI on different intents and responses. Regular data updates keep the AI aligned with changing customer behaviour, ensuring accurate and personalised interactions.

Choose a Learning Model

Picking the right model is a key step when building AI agents from scratch. There are two important types of models used in AI development:

• • Neural Networks : Neural networks mimic how the human brain processes information. They are great at handling large datasets and recognising patterns. If the AI agent needs to understand and respond to language, like a chatbot, neural networks are often the best fit.
• • Reinforcement Learning : Reinforcement learning helps AI agents learn through trial and error. It uses feedback from actions to adjust and improve over time. If the AI agent needs to make decisions or adapt based on user behaviour, reinforcement learning is a strong option.

Develop & Test AI Models

Once the AI framework is selected, the next step is building Natural Language Processing (NLP) models to handle customer queries. The AI needs to be trained on labelled datasets to recognise different intents, extract key details, and provide relevant responses.

Integrating AI with backend systems like CRM, inventory, and payment gateways is crucial for delivering real-time interactions. Testing with real user scenarios helps identify gaps and ensures the AI responds accurately. Regular model updates are also important to reduce false positives and improve how well the AI understands context.

Deploy & Optimise

Once the AI agent is built and tested, the next step is deploying it into a live environment. It is important to monitor how the AI handles real interactions and track key performance metrics.

How to know if AI is improving over time? Regular updates and feedback loops help refine responses and improve accuracy. Automated retraining ensures the AI stays relevant and adapts to changing customer behaviour and market trends. Ongoing optimisation keeps the AI delivering a smooth and effective customer experience.

No-Code vs. Build from Scratch: Which One is Right for E-commerce?

Now, it is time to think if an e-commerce business should go for a no-code builder or build the AI agent on their own.

Large e-commerce businesses with complex workflows or specific AI requirements should go for building their own AI agents. However, they should have in-house AI expertise, dedicated development teams, and a huge budget.

Is the goal to develop an AI that integrates deeply with custom systems and provides full control? Building from scratch is the right choice.

E-commerce businesses looking to adopt AI agents quickly and affordably might find no-code AI agent builders the perfect solution. Instead of spending months on development, they can launch prebuilt AI agents with advanced features.

Is the goal to improve customer experience and increase sales? Building AI from scratch can be complex and time-consuming. Solutions like TechMonk’s AI agent builder make it possible by handling the complexity and keeping operations smooth.