How to Build a Website Research Agent

May 5, 2025

Paul Omenaca

Customer Success at Stack AI

Websites are rich research opportunities, but analyzing an entire site is difficult. However, with the advent of AI agents, teams can now condense and extract information from entire sites in a matter of seconds.

This allows teams to leverage entire sites in their research operations, greatly increasing the volume of insights they can access.

In the following blog, we’ll show you how to build an AI agent that can research entire websites, and explain the benefits for your team.

What is an AI Agent?

An AI agent is a computational entity that perceives its environment through sensors and acts upon that environment using actuators, with the aim of achieving predefined objectives. Formally, an AI agent maps percept sequences to actions using a policy function, which can be hard-coded or learned from data.

This agent-environment interaction is typically modeled within frameworks such as reinforcement learning, where the agent receives feedback in the form of rewards or penalties, enabling it to optimize its behavior over time.

AI agents can be categorized along several dimensions: simple reflex agents rely on condition-action rules, model-based agents maintain internal representations of the world, goal-based agents plan actions to achieve specific outcomes, and utility-based agents make trade-offs to maximize expected utility.

More advanced architectures, such as deep reinforcement learning agents, leverage neural networks to approximate value functions or policies, enabling them to handle high-dimensional inputs like images or time-series data. Multi-agent systems extend this further, involving multiple agents that may cooperate or compete, requiring complex coordination and communication strategies.

In practical terms, AI agents underpin a wide range of intelligent systems, from autonomous drones and robotic process automation (RPA) bots to recommendation engines and trading algorithms. Their deployment involves challenges such as real-time decision-making, uncertainty modeling, explainability, and safety.

Developers often utilize toolkits like OpenAI Gym, RLlib, or TensorFlow Agents to design, simulate, and train these agents in virtual environments before real-world integration. As research in areas like meta-learning, transfer learning, and neuro-symbolic reasoning progresses, AI agents are poised to become even more adaptable, interpretable, and robust in complex domains.

Why Use an AI Agent to Research Websites?

Using an AI agent to research a website allows for a faster, more scalable, and intelligent approach to gathering and analyzing web data. Unlike manual research, which is time-consuming and prone to human error, AI agents can continuously monitor websites, extract relevant information, and respond to changes in real time.

This is especially valuable for tasks that involve large or frequently updated sites, such as news aggregators, e-commerce platforms, or competitor portals. By automating the data collection process, AI agents free up human researchers to focus on deeper analysis and strategic decision-making.

What sets AI agents apart from traditional web scrapers is their ability to interpret content contextually. Using technologies like natural language processing and machine learning, an AI agent can go beyond simple keyword matching to understand the intent and sentiment behind text, recognize patterns across multiple pages, and adapt to changes in structure or language.

For example, an AI agent could identify and categorize product reviews, detect shifts in customer sentiment, or summarize key updates from a company blog—all without needing constant manual adjustments.

Another key advantage of using AI agents for website research is their ability to integrate seamlessly into larger data workflows. Once information is gathered, the agent can analyze it, generate structured summaries, flag anomalies, or feed it into dashboards and reporting tools.

This end-to-end automation turns passive data into actionable insights, enabling organizations to react more quickly to market trends, emerging risks, or new opportunities. As the web continues to grow in complexity and volume, AI agents provide a vital tool for turning online information into strategic intelligence.