My Honest Take on AI Agents in May 2026

Hey everyone, Emma here from agent101.net!

It’s May 2026, and if you’re anything like me, your feed is probably swamped with talk about AI agents. It feels like every other day there’s a new framework, a new breakthrough, or a new debate about whether they’re going to take over the world (spoiler alert: probably not in the way some people think, at least not yet!).

A few months ago, I was feeling totally overwhelmed. I understood the basic concept – an AI that can perform tasks autonomously – but when it came to actually building one, or even just understanding how they were put together, I hit a wall. All the tutorials seemed to jump straight into complex architectures or assume you had a computer science degree. I just wanted to build something simple, something that actually did a thing, even if that ‘thing’ was just a glorified to-do list for my digital life.

That’s when I stumbled upon a really interesting challenge: building a personal summarization agent. Not just a tool that takes text and spits out a summary (there are a million of those), but an agent that could *decide* what *find* the content, and then *present* it to me in a useful format. I wanted something that felt like a tiny, helpful digital assistant, rather than just another API call.

So, today, I want to walk you through my journey of building a very, very basic, yet surprisingly effective, email summarization agent. This isn’t about becoming a full-stack AI engineer overnight; it’s about getting your hands dirty with the core concepts of an AI agent using tools that are accessible and relatively straightforward. Think of it as your first baby steps into the world of autonomous AI.

My Email Overload Problem (and Why I Needed an Agent)

Let’s be real: email is a beast. Especially if you subscribe to a lot of newsletters, industry updates, or are part of various online communities. My inbox often feels like a digital black hole where important information goes to die, buried under a mountain of “Did you know you left something in your cart?” notifications.

I tried all the usual tricks: filters, separate folders, unsubscribing from things I barely read. But the core problem remained: I still had to *open* emails, *scan* them, and *decide* if they were worth my time. This was a mental load I wanted to offload. I didn’t need an agent to write my emails, just to help me understand what was urgent or interesting without having to dive into every single one.

My goal was simple: an agent that could look at my incoming emails, figure out which ones seemed important or interesting based on a few rules, and then summarize those for me, perhaps even flagging specific actions. This isn’t a replacement for reading, but a pre-filter, a sort of “digital gatekeeper” to my attention.

Deconstructing a Simple AI Agent: The Core Components

Before we dive into the how-to, let’s quickly break down what makes an “agent” an agent, especially for a beginner project like this. Forget the sci-fi stuff for a minute. At its heart, an AI agent, even a simple one, has these characteristics:

1. Perception: It can observe its environment. In our case, that’s reading emails.
2. Reasoning/Decision-making: It can process that observation and decide what to do next based on some rules or a model. “Is this email important enough ?”
3. Action: It can perform an action in its environment. Summarizing the email, perhaps sending the summary to me.
4. Goal: It has an objective it’s trying to achieve. Reducing my email processing time.

For this project, we’re going to use Python (because, well, it’s Python – easy to read, tons of libraries) and a large language model (LLM) for the heavy lifting of summarization. Specifically, I’ve been playing with OpenAI’s API, but you could adapt this to other LLMs like Anthropic’s Claude or even local models if you’re feeling adventurous.

My first attempt was, frankly, a bit of a mess. I tried to build everything from scratch, including my own email parsing logic, and quickly got bogged down in the complexities of email headers and MIME types. Learn from my mistakes: use libraries!

Step 1: The “Perception” Layer – Reading Emails

The first hurdle was getting the agent to “see” my emails. I decided to focus on a specific folder – my “Newsletters” folder – to keep things manageable. Connecting directly to my main inbox felt a bit too risky for a first project, especially with sensitive work emails.

Python’s imaplib library is perfect for this. It lets you connect to an IMAP server (which most email providers use) and interact with your inbox. You’ll need your email address and an app-specific password (PLEASE DO NOT use your main email password for this! Most providers like Google, Outlook, etc., offer ways to generate these for third-party apps).

Code Snippet: Connecting to IMAP and Fetching Emails

import imaplib
import email
from email.header import decode_header
import os

# --- Configuration ---
EMAIL_ACCOUNT = os.getenv("EMAIL_ACCOUNT") # Store these securely, e.g., in environment variables
EMAIL_PASSWORD = os.getenv("EMAIL_APP_PASSWORD") 
IMAP_SERVER = "imap.gmail.com" # Or your email provider's IMAP server

def fetch_emails(folder_name="INBOX", num_emails=5):
 mail = imaplib.IMAP4_SSL(IMAP_SERVER)
 mail.login(EMAIL_ACCOUNT, EMAIL_PASSWORD)
 mail.select(folder_name)

 status, email_ids = mail.search(None, "UNSEEN") # Fetch unread emails
 email_id_list = email_ids[0].split()

 emails_to_process = []
 for i in email_id_list[-num_emails:]: # Process the most recent 'num_emails' unread ones
 status, msg_data = mail.fetch(i, "(RFC822)")
 for response_part in msg_data:
 if isinstance(response_part, tuple):
 msg = email.message_from_bytes(response_part[1])
 emails_to_process.append(msg)
 # Mark as seen if you want, or leave as unread
 # mail.store(i, '+FLAGS', '\\Seen') 
 mail.logout()
 return emails_to_process

def get_email_body(msg):
 body = ""
 if msg.is_multipart():
 for part in msg.walk():
 ctype = part.get_content_type()
 cdispo = str(part.get("Content-Disposition"))

 if ctype == "text/plain" and "attachment" not in cdispo:
 try:
 body = part.get_payload(decode=True).decode()
 break
 except:
 pass
 elif ctype == "text/html" and "attachment" not in cdispo:
 # If no plain text, try HTML (but be aware of formatting)
 if not body: # Only use HTML if plain text wasn't found
 try:
 body = part.get_payload(decode=True).decode()
 except:
 pass
 else:
 try:
 body = msg.get_payload(decode=True).decode()
 except:
 pass
 return body

# Example usage:
# if __name__ == "__main__":
# recent_emails = fetch_emails("INBOX", 3)
# for msg in recent_emails:
# subject, encoding = decode_header(msg["Subject"])[0]
# if isinstance(subject, bytes):
# subject = subject.decode(encoding if encoding else "utf-8")
# sender, encoding = decode_header(msg["From"])[0]
# if isinstance(sender, bytes):
# sender = sender.decode(encoding if encoding else "utf-8")
# print(f"Subject: {subject}")
# print(f"From: {sender}")
# # print(f"Body: {get_email_body(msg)[:200]}...") # Print first 200 chars
# print("-" * 30)

A quick note on get_email_body: parsing email bodies can be surprisingly tricky. Emails often come in multiple parts (plain text, HTML, attachments). This function tries to prioritize plain text and falls back to HTML if needed. For a real-world agent, you’d likely want more sophisticated HTML parsing (e.g., using BeautifulSoup) to remove all the junk.

Step 2: The “Reasoning” Layer – Deciding What

This is where our agent starts to show some intelligence. Instead of summarizing *every* email, we want it to apply some logic. For my initial agent, I set up a few simple rules:

• Sender Whitelist: Only summarize emails from specific senders (e.g., “[email protected]”, “[email protected]”). This is a crucial first filter.
• Keyword Check: Scan the subject and a portion of the body for keywords like “urgent,” “deadline,” “important update,” “new release.”
• Length Filter: Don’t bother summarizing tiny emails (“Ok,” “Thanks!”).

This is where the “agentic” part really comes in. It’s not just a script; it’s making a decision based on its perception.

Code Snippet: Simple Decision Logic

def should_summarize(msg, whitelist_senders, keywords):
 from_header = msg.get("From", "")
 subject_header = msg.get("Subject", "")
 body_preview = get_email_body(msg)[:500] # Check first 500 characters of body

 # Rule 1: Sender Whitelist
 is_whitelisted_sender = any(ws.lower() in from_header.lower() for ws in whitelist_senders)
 if not is_whitelisted_sender:
 # print(f"Skipping: Sender not whitelisted: {from_header}")
 return False

 # Rule 2: Keyword Check (in subject or body preview)
 has_keyword = any(kw.lower() in subject_header.lower() or kw.lower() in body_preview.lower() for kw in keywords)
 if not has_keyword:
 # print(f"Skipping: No relevant keywords found in subject/body: {subject_header}")
 return False
 
 # Rule 3: Length Filter (arbitrary, adjust as needed)
 if len(body_preview) < 100: # Don't summarize very short emails
 # print(f"Skipping: Email body too short: {subject_header}")
 return False

 return True

# Example usage:
# WHITELISTED_SENDERS = ["[email protected]", "[email protected]"]
# IMPORTANT_KEYWORDS = ["urgent", "action required", "new release", "critical update"]
#
# if __name__ == "__main__":
# # ... (fetch_emails as above)
# for msg in recent_emails:
# if should_summarize(msg, WHITELISTED_SENDERS, IMPORTANT_KEYWORDS):
# print(f"Decision: Summarize '{decode_header(msg['Subject'])[0][0].decode()}'")
# else:
# print(f"Decision: Skip '{decode_header(msg['Subject'])[0][0].decode()}'")

My initial keyword list was pretty basic. I quickly realized I needed to expand it to capture more nuanced topics relevant to my work, like specific framework names or project updates. This is where iterative improvement comes in: your agent gets "smarter" as you refine its rules.

Step 3: The "Action" Layer – Summarizing with an LLM

Now for the fun part: using an LLM to generate the summary. This is where the magic happens and where the agent truly becomes useful.

I chose OpenAI’s GPT-3.5-turbo for its balance of cost and performance. GPT-4 would be even better for quality, but for a daily summarizer, 3.5-turbo is usually sufficient.

Code Snippet: Summarizing with OpenAI API

from openai import OpenAI
import os

# --- Configuration ---
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
client = OpenAI(api_key=OPENAI_API_KEY)

def summarize_text(text_content, max_tokens=150):
 if not text_content.strip():
 return "No content ."

 try:
 response = client.chat.completions.create(
 model="gpt-3.5-turbo", # Or "gpt-4" for higher quality
 messages=[
 {"role": "system", "content": "You are a helpful assistant that summarizes emails concisely, highlighting key information and any action items. Keep the summary under 150 words."},
 {"role": "user", "content": f"Please summarize the following email content:\n\n{text_content}"}
 ],
 max_tokens=max_tokens,
 temperature=0.7 # Adjust for creativity vs. factualness
 )
 return response.choices[0].message.content.strip()
 except Exception as e:
 print(f"Error summarizing: {e}")
 return f"Could not summarize due to an error: {e}"

# Example usage:
# if __name__ == "__main__":
# sample_text = """
# Dear Emma,
# We are excited to announce a new update to the AgentFlow platform, version 2.1. This update includes improved
# integration with existing CRM systems and introduces a new visual workflow builder. Key features include:
# 1. Drag-and-drop interface for agent task sequencing.
# 2. Enhanced reporting for agent performance metrics.
# 3. Bug fixes and performance improvements.
# Please review the documentation at docs.agentflow.com/v2.1 by May 15th.
# Best regards,
# The AgentFlow Team
# """
# summary = summarize_text(sample_text)
# print(f"Summary: {summary}")

I found that the prompt engineering here is critical. Just asking "summarize this" often gives generic results. Adding instructions like "highlight key information and any action items" makes the summary much more useful for my specific goal.

Putting It All Together: The Agent Loop

An agent isn't just a one-shot script. It typically runs in a loop, perceiving, reasoning, and acting repeatedly. For my email agent, I schedule it to run every few hours using a simple cron job on my Linux machine, or a scheduled task on Windows.

Here’s how the main loop would look conceptually:

# main_agent.py

# ... (import functions from above) ...

WHITELISTED_SENDERS = ["[email protected]", "[email protected]", "[email protected]"]
IMPORTANT_KEYWORDS = ["urgent", "action required", "new release", "critical update", "security patch", "webinar", "event"]

def run_email_agent():
 print(f"[{datetime.now()}] Starting email agent run...")
 emails = fetch_emails(folder_name="INBOX", num_emails=10) # Adjust folder and count as needed
 
 summaries_generated = []

 for msg in emails:
 subject, encoding = decode_header(msg["Subject"])[0]
 if isinstance(subject, bytes):
 subject = subject.decode(encoding if encoding else "utf-8")
 sender, encoding = decode_header(msg["From"])[0]
 if isinstance(sender, bytes):
 sender = sender.decode(encoding if encoding else "utf-8")
 
 print(f"Processing email from '{sender}' with subject '{subject}'...")

 if should_summarize(msg, WHITELISTED_SENDERS, IMPORTANT_KEYWORDS):
 body_content = get_email_body(msg)
 if body_content:
 summary = summarize_text(body_content)
 summaries_generated.append({
 "subject": subject,
 "sender": sender,
 "summary": summary
 })
 print(f" -> Summarized. Summary: {summary[:100]}...") # Print first 100 chars of summary
 else:
 print(f" -> Skipped (empty body after parsing).")
 else:
 print(f" -> Skipped based on rules.")
 
 # --- Action 2: Present the summaries ---
 if summaries_generated:
 print("\n--- Daily Email Digest ---")
 for s in summaries_generated:
 print(f"Subject: {s['subject']}")
 print(f"From: {s['sender']}")
 print(f"Summary: {s['summary']}\n")
 # In a real agent, you might send this digest to yourself via another email,
 # a Slack message, or store it in a Notion database.
 else:
 print("No important emails found for summarization in this run.")
 print(f"[{datetime.now()}] Email agent run finished.")

if __name__ == "__main__":
 from datetime import datetime
 run_email_agent()

Right now, the "presentation" is just printing to the console. My next step is to integrate it with a messaging app like Slack or even send me a dedicated digest email. That would make it truly autonomous and useful without me having to manually check a log file.

My Takeaways & What I Learned

Building this simple email summarization agent was a fantastic learning experience. Here’s what truly clicked for me:

1. Start Small & Iterate: Don't try to build Skynet on day one. Focus on one specific problem and build the simplest possible agent to solve it. My first version just fetched emails; then I added the filter, then the summarization.
2. Libraries are Your Friends: Python's ecosystem is incredibly rich. Don't reinvent the wheel for email parsing or API calls.
3. Prompt Engineering is Powerful: The quality of your LLM's output heavily depends on how well you instruct it. Experiment with different system messages and user prompts.
4. The "Agent" is in the Decision-Making: It’s not just about using an LLM; it’s about the logic that decides *when* and *how* to use it. My should_summarize function is crude, but it's the core of its "intelligence."
5. Security Matters: Especially when dealing with email. Use app-specific passwords and environment variables for sensitive credentials.
6. It's a Glimpse into the Future: Even a basic agent like this gives you a real feel for how AI can automate mundane tasks and free up your attention. It's not about replacing humans, but augmenting them.

This little agent has already saved me a significant amount of time wading through newsletters. It's a small win, but it demonstrates the power of autonomous AI in a tangible way.

Your Turn: Actionable Takeaways

If you're looking to dip your toes into AI agents, here's what I recommend:

• Identify a Micro-Problem: What's a tiny, repetitive digital task you do daily or weekly? Summarizing specific types of content (articles, tweets, forum posts), cleaning up a spreadsheet, renaming files, monitoring a specific website for updates?
• Break it Down: How would you define the "perceive," "reason," and "act" steps for that problem?
• Pick Accessible Tools: Python, a free or low-cost LLM API (like OpenAI's free tier if available, or even local models like Llama 3 via Ollama), and relevant Python libraries.
• Code the Simplest Version: Don't aim for perfection. Get something working, even if it's clunky.
• Iterate and Refine: Watch your agent work (or fail!), and adjust its rules, prompts, or actions. This is how you learn!
• Prioritize Security: Always be mindful of API keys and personal data.

Don't be intimidated by the hype. Building an AI agent doesn't require a Ph.D. It requires curiosity, a willingness to tinker, and a bit of code. This journey has been incredibly rewarding for me, and I hope this walkthrough gives you the confidence to start your own agent adventure!

Let me know in the comments if you build something similar, or what your first agent project might be!

🕒 Published: May 3, 2026