The Computer-Use Researcher

The Computer-Use Researcher

Many real-world research tasks involve gathering information from multiple sources.

A typical workflow might involve:

1. searching for relevant sources
2. reading articles and documents
3. extracting key insights
4. summarizing findings into a report

This process can be time-consuming.

The Computer-Use Researcher is an AI agent designed to automate this workflow.

The agent can:

• search the web for information
• capture screenshots and visual data
• extract insights from charts and documents
• synthesize structured research reports

Conceptually:

Research Question
        ↓
Web Search
        ↓
Document Retrieval
        ↓
Visual Data Extraction
        ↓
Analysis
        ↓
Structured Report

This project demonstrates how multiple agent capabilities can be combined into a practical research assistant.

System Architecture

The research agent integrates several subsystems.

User Query
     ↓
Planning Agent
     ↓
Search Tool
     ↓
Document Analyzer
     ↓
Vision Module
     ↓
Report Generator

Each component handles a specific stage of the workflow.

Core Capabilities

The Computer-Use Researcher combines capabilities from earlier modules.

This makes the project a comprehensive agent application.

Step 1 — Web Search

The agent begins by searching the web for relevant sources.

Example query:

Research question:
What are the latest AI chip architectures?

The agent calls a search tool.

Example tool call:

{
  "tool": "web_search",
  "args": {
    "query": "latest AI chip architectures 2026"
  }
}

The results provide initial sources for analysis.

Example Search Tool

def web_search(query):

    results = search_api(query)

    return results

fn web_search(query: &str) -> Vec<String> {

    search_api(query)
}

The agent collects URLs and document summaries.

Step 2 — Document Retrieval

After identifying sources, the agent retrieves the content.

Example workflow:

Search results
      ↓
Select relevant sources
      ↓
Download article

Example document extraction:

article = fetch_article(url)

This text becomes part of the research context.

Step 3 — Capturing Visual Data

Many research sources contain important visual information such as:

• charts
• diagrams
• tables
• screenshots

The agent can capture visual data.

Example workflow:

Open webpage
      ↓
Capture screenshot
      ↓
Detect charts

This step uses visual grounding techniques from earlier modules.

Example Screenshot Tool

import pyautogui

def capture_screen():

    screenshot = pyautogui.screenshot()

    return screenshot

fn capture_screen() -> Image {

    screenshot::capture()
}

The screenshot can then be analyzed by a vision model.

Step 4 — Extracting Insights

The agent analyzes both text and visual data.

Example analysis tasks:

• summarizing articles
• extracting key statistics
• interpreting charts

Example prompt:

Summarize the key findings from the following article.

Example chart analysis:

Chart shows GPU market share:
NVIDIA 80%
AMD 15%
Intel 5%

These insights become part of the final report.

Step 5 — Synthesizing the Report

After gathering information, the agent synthesizes a research report.

Example structure:

Research Report

1. Overview of the topic
2. Key technologies
3. Market trends
4. Visual data analysis
5. Conclusions

The final output provides a structured summary of the research findings.

Example Report Generation

def generate_report(context, llm):

    prompt = f"""
    Generate a research report based on:
    {context}
    """

    return llm.generate(prompt)

fn generate_report(context: &str, llm: &LLM) -> String {

    let prompt = format!(
        "Generate a research report based on:\n{}",
        context
    );

    llm.generate(&prompt)
}

The report combines information from multiple sources.

Example End-to-End Workflow

Example execution:

User Question:
What are the latest AI GPU architectures?

Agent Workflow:
Search web
↓
Retrieve documents
↓
Capture charts
↓
Extract insights
↓
Generate research report

Final output:

Structured research report on AI GPU architectures.

Improving the Research Agent

Several enhancements can improve the system.

Examples include:

• multi-hop retrieval across sources
• automated citation generation
• fact verification using multiple sources
• chart-to-data extraction

These capabilities can transform the agent into a powerful AI research assistant.

Real-World Applications

Research agents can be useful in many domains.

These systems can dramatically accelerate information gathering.

The Role of Human Oversight

Despite their capabilities, research agents should still include human oversight.

Example workflow:

Agent generates report
      ↓
Human reviewer validates findings
      ↓
Final publication

Human review ensures reliability.

What This Project Demonstrates

The Computer-Use Researcher demonstrates how agent capabilities combine into a real system.

The project integrates:

• planning systems
• tool usage
• RAG retrieval
• computer-use automation
• reasoning models

This architecture represents a practical application of agentic AI.

Looking Ahead

In the next capstone project we will build a multi-agent coding pipeline, where specialized agents collaborate to design, implement, and review software.

→ Continue to 12.2 — The Multi-Agent Coding Pipeline