My First Steps Building an AI Agent: What I Learned

Hey everyone, Jake here from ClawGo.net! Hope you’re all having a productive start to your week. Mine certainly has been… interesting. More on that in a bit.

Today, I want to talk about something that’s been buzzing around my brain like a caffeinated wasp: the surprisingly tricky first steps of building your own AI agent. Not just thinking about it, or reading about it, but actually getting your hands dirty and making something work. Specifically, I’m focusing on those initial hurdles that make a lot of people throw their hands up and declare AI “too complicated.”

You see, I’ve been on a personal quest lately. My home office, while a sanctuary for coding and writing, has also become a bit of a digital graveyard for half-finished projects. My goal? To build a simple agent that monitors specific RSS feeds for new articles related to AI ethics, summarizes them, and then drafts a preliminary tweet thread for me to review. Sounds straightforward, right? Oh, you sweet summer child. I thought so too.

The “Getting Started” Trap: Why It’s Harder Than It Looks

When you read articles or watch demos, everything looks so… polished. They show you the finished product, the elegant code, the agent smoothly humming along. What they often skip are the hours spent debugging, the existential crises staring at error messages, and the sheer frustration of integrating different pieces that simply don’t want to play nice.

My first attempt at this RSS-to-tweet agent was, frankly, a disaster. I started with a grand vision: a sophisticated multi-agent system, each agent specializing in a different part of the pipeline. I spent a week designing the architecture, choosing fancy libraries, and even sketching out flowcharts. And then, I tried to write the first line of code.

That’s when I hit the first wall: environment setup. It’s always environment setup, isn’t it? Python versions, dependency conflicts, API keys that refuse to validate. It’s the digital equivalent of trying to assemble IKEA furniture with half the instructions missing and a screwdriver that’s slightly the wrong size.

Lesson One: Keep It Stupidly Simple (KISS)

My grand multi-agent system quickly devolved into a single, very confused script. And that, my friends, was the breakthrough. Instead of trying to orchestrate a symphony, I decided to play a single note really well.

For my RSS agent, I broke it down:

1. Fetch RSS feeds.
2. Parse new articles.
3. Send article content to an LLM for summarization.
4. Format the summary into a tweet thread.

Each step, initially, was a separate, isolated script. No fancy communication protocols, no complex orchestrators. Just a series of simple functions calling each other. This reduced the cognitive load immensely. When something broke, I knew exactly where to look.

Let’s talk about the fetching part. I initially tried some complex asynchronous libraries. Overkill. A simple `requests` call and `feedparser` worked perfectly well.

import requests
import feedparser

def fetch_rss_feed(url):
 try:
 response = requests.get(url, timeout=10)
 response.raise_for_status() # Raise an exception for bad status codes
 feed = feedparser.parse(response.content)
 return feed.entries
 except requests.exceptions.RequestException as e:
 print(f"Error fetching feed {url}: {e}")
 return []

# Example usage:
# articles = fetch_rss_feed("https://www.theverge.com/rss/index.xml")
# for entry in articles[:3]: # Just show first 3 for brevity
# print(f"Title: {entry.title}")
# print(f"Link: {entry.link}")
# print("-" * 20)

This snippet is deceptively simple, but getting here took me through a maze of SSL certificate errors and timeout issues. The point is, start with the most basic implementation you can imagine for each component.

The LLM Integration Conundrum

Once I had the RSS fetching down, the next big hurdle was getting the article content to an LLM and back. This is where most people get excited and then quickly discouraged. APIs, rate limits, context windows – it’s a lot to take in.

My initial thought was to dump the entire article text into the LLM. Bad idea. Many articles are long, and I quickly ran into context window limits and higher token costs. Plus, the summaries were often too generic.

Lesson Two: Pre-process and Prompt Smart

Before sending anything to the LLM, I realized I needed to do some pre-processing. For my ethics articles, I found that often the most relevant parts were in the first few paragraphs and the conclusion. A simple heuristic, not perfect, but good enough for a first pass.

More importantly, the prompt. Oh, the prompt. This is where the “agent” truly starts to take shape. I started with a very basic prompt: “Summarize this article.” The results were bland.

Then I started iterating. I added specific instructions:

• “Summarize this article about AI ethics.”
• “Focus on the ethical dilemmas presented.”
• “Keep the summary concise, no more than 150 words.”
• “Extract the main argument and any proposed solutions.”

And finally, for the tweet thread, I explicitly told it the format:

# Python (using a hypothetical LLM client, like OpenAI's or similar)

def summarize_and_tweet_draft(article_title, article_content, llm_client):
 # Simple pre-processing: take first 1000 chars or so to avoid context issues for initial draft
 # In a real agent, you'd want more intelligent chunking/summarization
 truncated_content = article_content[:1500] 

 prompt = f"""
 You are an AI ethics expert helping to draft social media content.
 Please summarize the following article and then draft a 3-tweet thread about it.

 Article Title: {article_title}
 Article Content: {truncated_content}

 Summary requirements:
 - Concise, under 100 words.
 - Focus on the core ethical issue and any key takeaways.

 Tweet Thread requirements:
 - 3 distinct tweets.
 - Each tweet should be under 280 characters.
 - Tweet 1: Introduce the article's main ethical point.
 - Tweet 2: Elaborate on a specific aspect or example from the article.
 - Tweet 3: Pose a question to encourage discussion or offer a concluding thought.
 - Use relevant hashtags like #AIEthics #TechPolicy #ResponsibleAI.
 """

 try:
 response = llm_client.chat.completions.create(
 model="gpt-4o", # or your preferred model
 messages=[
 {"role": "system", "content": "You are a helpful assistant."},
 {"role": "user", "content": prompt}
 ]
 )
 return response.choices[0].message.content
 except Exception as e:
 print(f"Error calling LLM: {e}")
 return "Failed to generate summary and tweets."

# Example usage (assuming 'llm_client' is initialized with your API key)
# llm_output = summarize_and_tweet_draft("AI and Bias in Healthcare", "...", my_llm_client)
# print(llm_output)

This iterative prompting is crucial. It’s less about finding the “perfect” prompt upfront and more about refining it through trial and error. Think of it as teaching a new intern – you wouldn’t just say “do this,” you’d give specific examples and guidelines.

The “Agent” Part: Orchestration, Not Over-Engineering

After getting the individual components working, I started thinking about how to tie them together into something I could actually call an “agent.” Again, my initial instinct was to reach for a complex framework. But I resisted.

For my simple RSS-to-tweet agent, the “orchestration” became a simple Python script that ran on a schedule. It didn’t need to be fancy. It just needed to:

1. Load a list of RSS feeds.
2. For each feed, fetch new articles (keeping track of what was already processed).
3. For each new article, call the summarization and tweet drafting function.
4. Store the drafted tweets for my review.

I used a simple JSON file to store the “last processed article link” for each feed. This kept things incredibly lightweight. No database needed for this initial version, no complex state management.

Lesson Three: Iterate and Expand Incrementally

The beauty of starting simple is that you can add complexity later, only when it’s absolutely necessary. Once my basic agent was running reliably, I could then think about:

• Adding more sophisticated article filtering (e.g., keyword matching).
• Integrating a proper database for tracking processed articles and drafted tweets.
• Building a simple web interface for reviewing and approving tweets.
• Adding a “critique” agent that reviews the drafted tweets for tone or accuracy before I see them.

But those are all V2, V3 features. My V1, which took me from zero to functional in about a week of evenings (after the initial over-engineering detour), is a simple script that gives me a daily digest of AI ethics news and pre-drafted tweet ideas. It’s not a full-blown autonomous entity, but it’s an incredibly useful tool that saves me a significant amount of time and mental energy.

Actionable Takeaways for Your First Agent

So, if you’re feeling that itch to build your own AI agent, but are daunted by the perceived complexity, here’s my advice, learned through personal trial and error:

1. Start with a Tiny Problem: Don’t try to automate your entire life. Pick one small, repeatable task that you find tedious or time-consuming.
2. Break It Down Brutally: Deconstruct your chosen task into the smallest possible, independent steps.
3. Implement Incrementally: Build each step as a standalone function or script first. Get it working perfectly in isolation before trying to connect it to anything else.
4. KISS (Keep It Stupidly Simple): Resist the urge to use the fanciest libraries or architectures. Use the simplest tools that get the job done. A few lines of Python can be an “agent” if it performs a task autonomously.
5. Iterate Your Prompts: If using an LLM, expect to spend time refining your prompts. It’s an art and a science. Experiment with different instructions and constraints.
6. Expect Frustration, Embrace Debugging: Errors are part of the process. View them as puzzles to solve, not roadblocks. Stack Overflow is your friend.
7. Don’t Be Afraid to Throw Away Code: My first attempt was mostly scrapped. That’s okay. Learning what doesn’t work is just as valuable as finding what does.

Building your first AI agent doesn’t require a PhD or a massive budget. It requires patience, a willingness to experiment, and a healthy dose of humility. My little RSS-to-tweet agent isn’t going to change the world, but it’s made my corner of it a little more efficient, and it’s taught me a ton about the practical realities of agent development. And that, for me, is a massive win.

What are you planning to automate first? Let me know in the comments!

🕒 Published: May 4, 2026