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How To Build Custom AI Agents For E-Commerce in 2026?

Updated on: Oct 16, 2025

Statista conducted a survey to understand how consumers want to use AI agents for online shopping. Around 50% to 60% of users say they would like to use AI for buying clothes, beauty products, and electronics.

What does that mean? AI Agents are becoming an important part of online shopping. It makes sales, support, marketing, and customer experiences better. That is why more e-commerce businesses should now focus on adopting AI agents into their operations.

But how can they build custom AI agents that match their specific needs? That is exactly what this blog will explore.

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 Simple Custom AI Agent Builder
• Building Custom AI Agents From Scratch

1. TechMonk: Custom AI Agent Builder

AgentMonk, TechMonk's AI Agent platform, is the ultimate solution for e-commerce companies looking to build AI Capital. AI Capital is a powerful portfolio of production-ready AI agents and software that can perceive, decide, and act across business workflows while improving over time. These agents work seamlessly with humans and systems, making large-scale personalisation a real advantage.

With advanced AI agents and agentic workflows, TechMonk helps you optimise every customer interaction and deliver personalised journeys. Using TechMonk's full-stack marketing toolkit, you can lower customer acquisition costs, boost lifetime value, increase repeat purchases, and unlock the full potential of AI-driven growth.

Tool Library: Pre-built & Custom Tools

TechMonk gives you a rich library of pre-built tools that power your custom AI agents and help them handle different tasks across the customer journey. These tools can manage customer inquiries, process orders, and generate leads. They improve your agents' capabilities without making you start from scratch.

The best part is that each tool is highly customizable to match your unique business needs. Here's a look at some of the pre-built tools ready to power your AI agents:

But what if you need something more specific? TechMonk also lets you build your own custom tools to enhance the functionality of your AI agents. You can create tools that meet your exact requirements, and they integrate smoothly with your workflow.

So, how do you build a custom tool? Let me show you.

1. Open TechMonk's Agent Builder, go to the Tool Library, and click Create New Tool under the Custom Tools section.

2. Enter the tool name, description, and type, then fill in the required fields and click Next.

3. Add custom code (if needed), enter button details, and click Done to save the tool.

Agent Library: AI Agents to Streamline Every Workflow

With TechMonk, you can create AI agents that fit your specific workflows using the tools available in the system. These agents handle specific tasks, automate processes, and improve efficiency across your operations. You also get the flexibility to build custom AI solutions for any business need directly from the agent library.

Here's how you can build your own custom AI agents.

1. Open Agent Library, go to Custom Agents, and click Create New Agent.

2. Enter the agent's Name, Description, and Output in the pop-up window.

3. Choose tools from the Tool Library, then click Done to save the AI agent.

How do you make sure your custom AI agents perform exactly as you want? TechMonk makes it easy.

• 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.
• Strict Guardrails: Set input and output guardrails to allow only valid inputs. This ensures accurate responses and prevents misuse or prompt-hacking, keeping quality control strong.
• Testing Automation: Use an LLM Judge to train and validate your AI agents' responses anytime. This ensures your customers always get accurate and relevant answers.
• Observability of Workflows: Watch how your AI agents handle customer queries in real time. Spot flaws, optimise their interactions, and deliver better customer experiences.
• Traceability of Conversations: Track AI agent conversations across channels. See how responses are generated and quickly identify areas for improvement.
• Tracking AI Agent Performance: Measure reasoning, response times, and latency to get clear insights. Use this data to continuously improve efficiency and results.

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.

Agent Builder 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.

E-commerce businesses and enterprises 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 AgentMonk make it possible by handling the complexity and keeping operations smooth.

How to Build Custom AI Agents Using TechMonk (Step-by-Step)