AI Employee for Research: How to Build an Agent That Reads 100 Articles a Day

Information overload isn’t a productivity problem — it’s an opportunity cost problem. Every article you don’t read might contain the insight that changes your strategy, the competitor move you need to counter, or the market signal you should act on.

The solution isn’t reading faster. It’s building an AI agent that reads for you.

In this guide, we’ll build a research agent that monitors your chosen sources, reads and summarizes 100+ articles per day, extracts key insights, stores them in a searchable knowledge base, and sends you a concise daily briefing. This is one of the most practically valuable AI Employees you can build — and it’s simpler than you think.

What the Finished Agent Does

Before we build, let’s see the end result. Every morning at 7:30 AM, you receive a briefing like this:

📰 Daily Research Briefing — 21 Feb 2026
Sources monitored: 47 | Articles processed: 112 | Reading time saved: ~6 hours

🔴 HIGH PRIORITY (3 items)
1. MAS announces new AI governance framework for financial institutions
   → Impacts: compliance requirements, deadline Q3 2026
   → Source: CNA Business, 6:14 AM
   
2. OpenAI releases GPT-5 with native tool use
   → Impacts: agent capability, competitor landscape  
   → Source: TechCrunch, 2:30 AM
   
3. Singapore GDP growth revised upward to 3.8%
   → Impacts: market sentiment, hiring outlook
   → Source: Straits Times, 7:00 AM

🟡 NOTABLE (8 items)
[Summaries with key takeaways...]

🟢 FYI (23 items)  
[One-line summaries, expandable in knowledge base...]

📊 Weekly Trends: AI regulation (+45% mention frequency), 
semiconductor supply (+22%), Singapore startup funding (-8%)

This briefing is generated automatically. No human involvement. The agent processed 112 articles, classified them by relevance to your interests, extracted actionable insights, and delivered a briefing before you finished your kopi.

The Architecture

The research agent has five components working in a pipeline:

[Sources] → [Collector] → [Analyzer] → [Knowledge Base] → [Briefing]
   RSS         Fetch &        LLM          Vector DB         Daily
   APIs        Parse          Summary      + Metadata        Report
   Search                     + Classify                     Email/
                                                             Telegram

Let’s build each one.

Step 1: Define Your Sources

The quality of your briefing depends entirely on the quality of your sources. Start by listing 20-50 sources in three categories:

Primary Sources (check every hour)

These are critical to your work. For a Singapore business professional, this might be:

• CNA Business
• Straits Times Business
• MAS announcements
• Your industry’s top 3-5 publications

Secondary Sources (check every 4 hours)

Important but less time-sensitive:

• TechCrunch, The Verge (for tech industry)
• Reuters Business, Bloomberg (for finance)
• Industry-specific newsletters
• Competitor blogs and press releases

Discovery Sources (check daily)

For serendipitous finds and emerging trends:

• Hacker News (top stories only)
• Reddit (specific subreddits: r/singaporefi, r/singapore, r/artificial)
• Academic preprint servers (arXiv for AI/ML)

Store your sources in a configuration file:

# sources.yml
sources:
  - name: "CNA Business"
    url: "https://www.channelnewsasia.com/business"
    type: "rss"
    check_interval: 60  # minutes
    priority: "high"
    
  - name: "Straits Times Business"
    url: "https://www.straitstimes.com/business"
    type: "web_scrape"
    check_interval: 60
    priority: "high"
    
  - name: "TechCrunch AI"
    url: "https://techcrunch.com/category/artificial-intelligence/feed/"
    type: "rss"
    check_interval: 240
    priority: "medium"

Start with 20 sources. You can always add more. More isn’t always better — signal-to-noise ratio matters more than volume.

Step 2: Build the Collector

The collector fetches articles from your sources and extracts readable text. Two main approaches:

RSS Feeds (Preferred)

Many news sites publish RSS feeds. These are structured, reliable, and easy to parse.

import feedparser

def collect_rss(feed_url):
    feed = feedparser.parse(feed_url)
    articles = []
    for entry in feed.entries:
        articles.append({
            "title": entry.title,
            "url": entry.link,
            "published": entry.published,
            "summary": entry.get("summary", ""),
        })
    return articles

Web Fetching (For Sites Without RSS)

For sources that don’t offer RSS, use web fetching to extract article content:

# Using a web fetch tool (like Brave API or similar)
def collect_web(url):
    content = web_fetch(url, extract_mode="markdown")
    # Parse out individual article links
    article_links = extract_article_urls(content)
    
    articles = []
    for link in article_links[:10]:  # Limit to avoid overloading
        article_content = web_fetch(link, extract_mode="markdown")
        articles.append({
            "title": extract_title(article_content),
            "url": link,
            "content": article_content,
        })
    return articles

Deduplication

Articles often appear across multiple sources. Before processing, deduplicate by URL and by title similarity. A simple approach:

def is_duplicate(new_article, existing_articles):
    # Exact URL match
    if new_article["url"] in [a["url"] for a in existing_articles]:
        return True
    # Fuzzy title match (catches same story from different outlets)
    for existing in existing_articles:
        if title_similarity(new_article["title"], existing["title"]) > 0.85:
            return True
    return False

Step 3: The Analyzer (LLM Processing)

This is where the AI does the heavy lifting. Each article gets processed through the LLM for:

1. Summarization — compress the article to key points
2. Classification — categorize by topic and relevance
3. Insight extraction — pull out actionable takeaways
4. Priority scoring — how important is this for you specifically?

analysis_prompt = """
Analyze this article for a Singapore-based [your role] 
focused on [your interests].

Article: {article_content}

Provide:
1. SUMMARY (2-3 sentences, key facts only)
2. CATEGORY (choose: industry, competitor, regulation, technology, 
   market, other)
3. RELEVANCE (1-10, where 10 = directly impacts my work)
4. KEY INSIGHTS (bullet points, actionable takeaways only)
5. ENTITIES (companies, people, regulations mentioned)
6. SENTIMENT (positive/negative/neutral for my industry)

Be concise. No filler.
"""

Cost optimization tip: Not every article needs full GPT-4/Claude analysis. Use a cheaper, faster model (GPT-4o-mini, Claude Haiku) for initial relevance screening. Only send high-relevance articles to the full model for deep analysis. This can cut costs by 70%.

# Two-stage processing
quick_relevance = fast_model.classify(article, your_interests)

if quick_relevance > 6:  # Worth deep analysis
    full_analysis = main_model.analyze(article, analysis_prompt)
else:
    # Store with minimal metadata, skip deep analysis
    store_as_low_priority(article, quick_relevance)

Step 4: The Knowledge Base

This is what makes your research agent compound value over time. Every article, summary, and insight gets stored in a searchable knowledge base.

Storage Architecture

knowledge_base/
├── articles/          # Full article text + metadata
├── summaries/         # LLM-generated summaries
├── insights/          # Extracted insights, tagged
├── entities/          # Companies, people, regulations
└── trends/            # Weekly trend aggregations

Vector Database for Semantic Search

Store article summaries as vector embeddings. This lets you ask natural language questions across your entire knowledge base:

• “What has been written about MAS regulation changes in the past month?”
• “Any competitor announcements related to AI products?”
• “Summarize everything I’ve collected about the Singapore property market this quarter.”

Tools: ChromaDB (free, local), Pinecone (hosted, scalable), or even a simple SQLite database with full-text search for smaller knowledge bases.

Metadata That Matters

For each article, store:

• Source, URL, publish date
• LLM summary and insights
• Relevance score
• Category and entities
• Whether you’ve read the full article (for follow-up)

This metadata turns your knowledge base into a powerful research tool. Six months in, you’ll have thousands of analyzed articles — a personalized, searchable archive of everything relevant to your work.

Step 5: The Daily Briefing

The briefing generator runs once daily (or more frequently if you prefer). It:

1. Queries the knowledge base for articles processed in the last 24 hours
2. Ranks them by relevance score
3. Groups them by category
4. Generates a structured briefing using the LLM
5. Identifies weekly trends by comparing this week’s topics to last week’s

Delivery options:

• Email — most professional, good for teams
• Telegram bot — instant, mobile-friendly, what many Singapore professionals prefer
• Slack — for team-wide briefings
• Dashboard — web-based, interactive (more complex to build)

def generate_briefing():
    # Get today's analyzed articles
    today = get_articles_since(hours=24)
    
    high_priority = [a for a in today if a["relevance"] >= 8]
    notable = [a for a in today if 5 <= a["relevance"] < 8]
    fyi = [a for a in today if a["relevance"] < 5]
    
    # Generate trend analysis
    this_week_topics = get_topic_frequencies(days=7)
    last_week_topics = get_topic_frequencies(days=14, exclude_days=7)
    trends = compare_trends(this_week_topics, last_week_topics)
    
    # Compose briefing with LLM
    briefing = llm.generate(briefing_template, {
        "high_priority": high_priority,
        "notable": notable,
        "fyi_count": len(fyi),
        "trends": trends,
        "total_processed": len(today),
    })
    
    send_telegram(briefing, chat_id=YOUR_CHAT_ID)

Scheduling: Putting It All Together

Use cron jobs to run each component on its schedule:

# Collect from high-priority sources every hour
0 * * * * python collect.py --priority=high

# Collect from medium-priority sources every 4 hours
0 */4 * * * python collect.py --priority=medium

# Collect from low-priority sources once daily at 6 AM
0 6 * * * python collect.py --priority=low

# Run analysis pipeline every 2 hours
0 */2 * * * python analyze.py

# Generate and send daily briefing at 7:30 AM SGT
30 7 * * * python briefing.py

# Weekly trend report every Monday at 8 AM
0 8 * * 1 python weekly_trends.py

Real-World Performance

A well-configured research agent typically:

• Processes 80-150 articles per day from 30-50 sources
• Costs S$15-40/month in API calls (with two-stage processing)
• Saves 4-6 hours per day of manual reading and monitoring
• Catches 95%+ of relevant news that a human would find through manual browsing
• Gets better over time as the knowledge base grows and you tune the relevance criteria

The 5% it misses? Usually paywalled content, information shared verbally at conferences, or extremely niche sources. For those, you still need human networks. AI handles the volume; you handle the nuance.

Limitations to Be Honest About

It summarizes, not understands. The AI can tell you what an article says. It can’t always tell you why it matters for your specific situation. The high-priority flags are based on keyword and topic matching, not deep strategic understanding.

Garbage sources produce garbage briefings. If you feed it low-quality sources, you’ll get noise. Curate your source list carefully and review it monthly.

It can hallucinate connections. Occasionally the LLM will draw connections between articles that don’t actually exist. Always click through to the source for high-stakes decisions.

It’s not real-time. With hourly collection, you might be 30-60 minutes behind breaking news. For truly time-sensitive monitoring (trading, crisis management), you need streaming solutions, not batch processing.

What You’ll Build at AI Academy

This research agent is one of the core projects in AI Academy’s advanced courses. You’ll build it from scratch with expert guidance, including:

• Optimizing the collection pipeline for Singapore-specific sources
• Fine-tuning relevance scoring for your industry
• Building the knowledge base with proper vector search
• Connecting delivery channels (Telegram, email, Slack)
• Scaling to team-wide research operations

This is what we mean by building AI Employees — not chatbots that answer questions, but agents that do real work autonomously. The research agent is often the first one our students build, and it becomes the foundation for more specialized agents like financial research tools and industry-specific assistants.

Ready to build your own AI research agent? Join AI Academy’s next cohort →