I Built My AI Agent to Automate a Specific Task

Hey there, Clawgo faithful! Jake Morrison here, back at the keyboard and buzzing about something I’ve been tinkering with relentlessly over the past few weeks. You know me, I love getting my hands dirty with the latest AI tech, especially when it promises to make my life, and yours, a little bit easier. Today, we’re not just talking about AI; we’re diving headfirst into the practical magic of getting an AI agent to actually *do* stuff for you. Specifically, we’re going to talk about a very focused angle: building a simple, autonomous research agent that helps you stay on top of a niche topic. Because let’s be real, the firehose of information out there is enough to drown anyone.

I’ve been trying to keep up with developments in AI ethics, specifically around bias detection in large language models. It’s a field moving at light speed, and every other day there’s a new paper, a new discussion, a new framework. My manual process involved endless RSS feeds, Google Scholar alerts, and a mountain of unread tabs. It was unsustainable. I needed a better way. And that’s where the idea for a dedicated, always-on research agent really clicked for me.

My Information Overload Problem, Solved (Mostly) by an Agent

Let me paint a picture. It’s 7 AM. My coffee is brewing, and I’m already feeling that familiar dread of opening my browser. Twenty tabs from yesterday, another dozen articles pushed to my ‘read later’ app, and a nagging feeling that I’ve missed something crucial. This isn’t productive. It’s just…noise. My goal wasn’t to replace my own critical thinking, but to delegate the initial sifting and sorting. I wanted a digital assistant that understood my specific interests and could bring me the highlights, not the whole ocean.

This isn’t about some sci-fi general AI that folds your laundry and writes your blog posts (though wouldn’t that be nice?). This is about a very specific, task-oriented agent that takes a defined input, processes it according to a set of rules, and gives you a digestible output. Think of it as your personal librarian, but one who reads everything overnight and leaves a concise summary on your digital doorstep.

Why Not Just Use Google Alerts?

Good question! And one I asked myself. Google Alerts are great for keyword matching, but they lack nuance. I’d get flooded with articles that mentioned “AI bias” but were about, say, image recognition bias, when my focus was specifically on LLMs. I needed something that could understand context, filter out irrelevant noise, and even synthesize information. A simple keyword search just doesn’t cut it when you’re looking for deeper insights or emerging trends.

My agent, which I’ve affectionately named “Clawdette” (because, you know, Clawgo), goes beyond keywords. It uses a language model to understand the *meaning* of the content, not just the words. This makes a huge difference in the quality of the information it surfaces.

Building Clawdette: The Guts of My Research Agent

Alright, let’s get into the nitty-gritty. I’m going to walk you through the basic components I used to build Clawdette. This isn’t rocket science, but it does require a bit of scripting and an understanding of how these AI models work. My setup uses a combination of Python for scripting, a couple of open-source libraries, and access to an LLM API (I used OpenAI’s GPT-4, but you could adapt this for others).

Step 1: The Information Gathering Module

First, Clawdette needs to gather information. For my specific use case, this meant monitoring academic papers, news articles, and specific discussion forums. I used a two-pronged approach:

1. RSS Feed Scraper: Many academic journals and tech news sites offer RSS feeds. This is a super efficient way to get new content without constantly hitting their servers. I used the feedparser Python library.
2. Simple Web Scraper (for specific sites): For sites without good RSS feeds, I built a basic scraper using BeautifulSoup and requests to pull content from specific URLs I identified as important sources.

Here’s a simplified snippet of how I’d fetch articles from an RSS feed:

import feedparser
import requests
from bs4 import BeautifulSoup

def get_articles_from_rss(rss_url):
 feed = feedparser.parse(rss_url)
 articles = []
 for entry in feed.entries:
 # Basic filter for recent articles (e.g., last 24 hours)
 # You'd need more solid date parsing for production
 articles.append({
 'title': entry.title,
 'link': entry.link,
 'summary': getattr(entry, 'summary', 'No summary available')
 })
 return articles

def scrape_article_content(url):
 try:
 response = requests.get(url, timeout=10)
 response.raise_for_status() # Raise an exception for HTTP errors
 soup = BeautifulSoup(response.text, 'html.parser')
 # This is highly dependent on the website's structure
 # You'll need to inspect the HTML to find the main content
 main_content_div = soup.find('div', class_='article-body') 
 if main_content_div:
 return main_content_div.get_text(separator='\n', strip=True)
 return "Content not found."
 except requests.exceptions.RequestException as e:
 print(f"Error scraping {url}: {e}")
 return None

# Example usage:
# tech_rss = "https://example.com/tech-news-rss.xml"
# new_articles = get_articles_from_rss(tech_rss)
# for article in new_articles:
# print(f"Title: {article['title']}\nLink: {article['link']}")
# full_content = scrape_article_content(article['link'])
# # Now you have the full content to pass to your LLM

A quick word of caution: When scraping, always be mindful of a website’s robots.txt file and terms of service. Don’t hammer a site with requests, and respect their rules. I generally focused on sites that either provided clear APIs or were academic archives with explicit open access policies.

Step 2: The Filtering and Summarization Module (The AI Brain)

This is where the magic happens. Once Clawdette has a pile of raw articles, it needs to figure out which ones are relevant and then summarize them. This is where the LLM comes in. My prompt engineering here was crucial. I didn’t just ask “summarize this.” I gave it specific instructions:

• “You are an expert AI ethics researcher focused on bias detection in large language models.”
• “Read the following article. First, determine if its primary focus is on novel methods for detecting or mitigating bias in LLMs, or if it presents new findings on LLM bias. If not, discard it.”
• “If relevant, provide a concise summary (max 200 words) highlighting the key methodology, findings, and implications for LLM development. Also, identify any novel datasets or benchmarks introduced.”
• “Finally, assign a ‘relevance score’ from 1 to 5, where 5 is highly relevant and immediately actionable for someone researching this topic.”

This structured prompting ensures that the LLM acts like a specialized expert, not just a general summarizer. It saves me from reading summaries of irrelevant articles.

from openai import OpenAI
# client = OpenAI(api_key="YOUR_OPENAI_API_KEY") # Replace with your actual key

def process_article_with_llm(article_title, article_content):
 prompt = f"""
 You are an expert AI ethics researcher focused on bias detection in large language models.
 Your task is to analyze the following article:

 Title: "{article_title}"
 Content: "{article_content}"

 First, determine if its primary focus is on novel methods for detecting or mitigating bias in LLMs,
 or if it presents new findings on LLM bias. If its primary focus is NOT this, indicate 'Irrelevant'
 and provide no further analysis.

 If relevant, provide a concise summary (max 200 words) highlighting the key methodology, findings,
 and implications for LLM development. Also, identify any novel datasets or benchmarks introduced.
 Finally, assign a 'relevance score' from 1 to 5, where 5 is highly relevant and immediately actionable
 for someone researching this topic.

 Format your output clearly, separating sections.
 """

 try:
 response = client.chat.completions.create(
 model="gpt-4-turbo-preview", # Or your preferred model
 messages=[
 {"role": "system", "content": "You are a helpful assistant."},
 {"role": "user", "content": prompt}
 ],
 temperature=0.3 # Lower temperature for more factual, less creative output
 )
 return response.choices[0].message.content
 except Exception as e:
 print(f"Error processing article with LLM: {e}")
 return "LLM processing failed."

# Example usage:
# article_content = scrape_article_content(article['link']) # From previous step
# if article_content:
# llm_analysis = process_article_with_llm(article['title'], article_content)
# print(llm_analysis)

Temperature setting: Notice the temperature=0.3. For factual tasks like summarization and filtering, you want a lower temperature. This makes the LLM less “creative” and more focused on providing direct, consistent answers based on the input.

Step 3: The Output and Notification Module

Finally, what good is all this analysis if it just sits in a Python script? Clawdette needed to deliver its findings. I explored a few options:

• Email Digest: Simple, effective. I configured a script to send me an HTML-formatted email every morning with the summaries of the most relevant articles (relevance score 4 or 5).
• Slack/Discord Notification: For quicker, more immediate alerts on extremely high-relevance items, I set up a webhook to my private Slack channel.
• Local Markdown File: As a backup, and for a cumulative record, I also had Clawdette append findings to a local Markdown file, organized by date.

For the email digest, I built a simple HTML string in Python and used the smtplib library. For Slack, it’s even easier with their incoming webhooks.

import json
import requests

def send_slack_notification(webhook_url, message):
 payload = {
 "text": message
 }
 try:
 response = requests.post(webhook_url, json=payload)
 response.raise_for_status()
 print("Slack notification sent successfully.")
 except requests.exceptions.RequestException as e:
 print(f"Error sending Slack notification: {e}")

# Example usage (after LLM processing):
# if "relevance score: 5" in llm_analysis.lower(): # Simple check
# slack_message = f"🚨 HIGH RELEVANCE ALERT: {article_title}\n\n{llm_analysis}\n\nRead more: {article_link}"
# slack_webhook = "YOUR_SLACK_WEBHOOK_URL"
# send_slack_notification(slack_webhook, slack_message)

My Takeaways and What I Learned

Building Clawdette wasn’t just a fun project; it genuinely changed how I approach staying informed. Here are my key takeaways:

• Specificity is King: The more focused your agent’s task and the more precise your prompts, the better its performance will be. A general “research everything” agent is likely to be overwhelmed and underperform.
• Iterate on Prompts: My initial prompts for Clawdette were nowhere near as good as the ones I shared above. I refined them over days, observing the output and adjusting the instructions until I got exactly what I wanted. Think of prompt engineering as teaching, not just asking.
• Error Handling is Crucial: Websites change, APIs go down, the internet blips. Build in solid error handling for your scrapers and API calls, or your agent will constantly crash.
• Start Small, Expand Later: Don’t try to build the ultimate AI assistant on day one. Start with a single, achievable task, get it working reliably, then add features. My first version of Clawdette just summarized articles; the filtering by relevance came later.
• It’s a Tool, Not a Replacement: Clawdette doesn’t do my thinking for me. It does the grunt work of sifting and summarizing, allowing me to spend my precious mental energy on understanding, analyzing, and synthesizing the *most important* information. It augmented my workflow, it didn’t automate my intelligence.

The beauty of building your own agent like this is the control you have. You decide the sources, the filtering criteria, and the output format. You’re not beholden to someone else’s algorithm or their idea of what’s relevant.

So, if you’re drowning in information, feeling the pressure to keep up, or just curious about what these AI agents can *really* do, I highly encourage you to pick a specific problem and try building a simple agent to tackle it. It’s a fantastic way to learn, and you might just solve one of your own persistent headaches.

Until next time, keep clawing your way to smarter tech!

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🕒 Last updated: March 26, 2026  ·  Originally published: March 23, 2026