How to Build an AI Web Research Agent

In our journey to build a cohesive AI ecosystem, we’ve assembled several specialized agents. We have an agent for handling emails, another for managing Google Drive, and a third for organizing our calendar. Each is a critical component, but the system remains inwardly focused.

To achieve a higher level of automation, our system needs to interact with the outside world. It needs the ability to access the internet to conduct research on our behalf. While you could simply provide a generic web search tool, this is a Level 2 approach — functional, but limited. To move toward Level 4, where systems integrate, we need a dedicated nano-agent to handle all web queries with precision.

Today, we will build that web agent.

Designing the Web Agent’s Toolkit

A capable research agent requires more than a single tool. It needs a curated toolkit to handle different types of queries effectively. For this build, we will equip our agent with three distinct tools:

• Perplexity: For deep research, understanding multiple perspectives, and synthesizing answers from various sources.
• Tavily: For quick, factual searches, retrieving recent news, or verifying specific pieces of information.
• OpenWeatherMap: For a specialized task — retrieving current weather data for any given location.

This multi-tool approach is crucial. It prevents the agent from using a complex research tool for a simple query, saving time and resources.

Defining the Agent’s Core Logic

With the tools in place, we define the agent’s operational logic through a simple, direct prompt. The core instruction is: “You are a web research assistant. Your responsibilities include conducting quick web searches, performing deeper research, and retrieving current weather information.”

We then provide clear directives on when to use each tool:

• For web search and research: Use Tavily for quick facts and recent news. Use Perplexity when a question requires in-depth research or synthesized answers.
• For weather inquiries: Use OpenWeatherMap to retrieve current weather data, including temperature, conditions, and forecasts.

This clear, rule-based logic ensures the agent operates efficiently, selecting the right tool for each specific task without ambiguity.

Testing the Agent’s Capabilities

Let’s test the agent with a practical business query. I will ask it to perform a high-level search, instructing it not to spend too much time.

The query: 

“Can you search online for the most recent training and n8n workflows for business automation? Don’t spend too much time on this, just high level.”

The master agent correctly routes the request to our newly built web agent. The web agent then queries its tools to gather the necessary information. Once the research is complete, the findings are processed and delivered.

Here are the results:

• Lead Capture to CRM with Enrichment and AI Scoring
• Social Media Publishing, Scheduling, and Brand Mention Monitoring
• E-commerce Order to Invoice Processing
• Customer Onboarding Journeys
• Support Ticket Triage with Intent Tagging and Auto-Routing
• Data Sync and Nightly Backups Across CRM, Sheets, and Storage
• Report Pipelines to Dashboards and Slack Alerts
• HR Recruiting Intake, Resume Parsing, and Interview Scheduling

The output is concise, relevant, and directly answers the query. It demonstrates the agent’s ability to quickly synthesize information from the web, providing actionable insights that a business leader can use. 

Watch the fill video here - https://youtu.be/DFAmNsaSs9M

This build is a foundational step toward Level 4 automation, where specialized agents like this one are integrated into a larger, more autonomous system.

The setup for this agent can be complex. If you are not tech-savvy, I will have the full detailed instructions in our Corporate Automation Library (CAL), which hosts the n8n code and steps required to get this running on your server. We have over 50 high-impact, high-ROI automations, with 2–4 new corporate automations uploaded weekly.

Click Here to gain access to CAL.

Next, we will focus on a content agent, which will have the ability to create and edit images, convert images to video, and generate video content.

Ritesh Kanjee | Automations Architect & Founder Augmented AI 

(121K Subscribers | 58K LinkedIn Followers)