Polling vs Webhooks: Which Approach is Better for AI Message Monitoring?

Mar 21, 2026

A Reddit post recently sparked a heated debate in the AI integration community. A developer admitted to using polling instead of webhooks for their Claude-Teams integration. The reason? “Didn’t want to deal with the Graph API, webhooks, Azure AD, or permissions.”

The comments were brutal: “This polling method seems very wasteful to do all that work every 2 minutes all day and night.”

But here’s the thing—sometimes the “dumb” approach is actually the smart one. Let me explain why.

The Problem I Faced

I wanted to build an AI agent that monitors Microsoft Teams messages and responds automatically using Claude. The “proper” way would be:

Register an app in Azure AD
Configure OAuth permissions
Set up a webhook endpoint with HTTPS
Handle authentication tokens
Process push notifications

Instead, I wrote a 20-line Python script that checks for new messages every 2 minutes. It worked in an afternoon.

Was I wrong? Let’s dig into the trade-offs.

What Polling Actually Does

Polling is straightforward: your code repeatedly asks the API “anything new?” at fixed intervals.

┌─────────────┐     GET /messages?since=last_check     ┌─────────────┐
│   Your AI   │ ─────────────────────────────────────▶│   Platform  │
│   Agent     │◀───────────────────────────────────────│   API      │
│             │     Response: new messages (or empty)   │             │
└─────────────┘                                        └─────────────┘
       │
       ▲
       │ Every 2 minutes
       └─────────────────────┐

The client initiates every request. No server infrastructure needed. No complex authentication flows. Just a simple loop.

The downside? Inherent latency. If you poll every 2 minutes, the average delay is 1 minute. For high-volume systems, you’re making lots of unnecessary API calls.

What Webhooks Actually Require

Webhooks flip the model: the platform pushes data to you when events occur.

┌─────────────┐      POST /webhook endpoint      ┌─────────────┐
│   Platform  │ ─────────────────────────────────▶│   Your AI   │
│   (Teams,   │     {event: "message", ...}      │   Agent     │
│    Slack)   │                                    │   Server   │
└─────────────┘                                    └─────────────┘
       │
       ▲
       │ Event triggers immediately
       └───────────────────────────────┐

Sounds elegant. But the setup complexity is significant:

Public endpoint required: Your server must be accessible from the internet
HTTPS mandatory: No self-signed certificates
Authentication handling: Verify signatures, manage OAuth tokens
Platform-specific registration: Azure AD, Slack app setup, Discord bot registration

For my Teams integration, webhooks would require Azure AD app registration, permission scopes, and OAuth token refresh logic. That’s hours of work before writing a single line of message-handling code.

The Reddit Debate: Who Was Right?

The criticism focused on resource waste: “all that work every 2 minutes all day and night.”

Let’s do the math for a personal tool:

Polling approach:

720 API calls per day (every 2 minutes)
Maybe 10 of those return actual messages
Total “waste”: 710 empty polls

Webhook approach:

10 events per day (actual messages)
But: 8+ hours of initial setup
Ongoing server maintenance
Certificate renewals
Security patches

For a tool handling 10 messages daily, the “waste” of polling is negligible. The webhook approach trades cheap API calls for expensive developer time.

At scale, this calculation flips. If you’re processing thousands of messages per hour, webhooks become essential. But for prototypes and personal tools? Polling is pragmatically efficient.

Code Comparison: The Reality Check

Here’s the polling implementation that took me 30 minutes:

import time
import requests
from anthropic import Anthropic

POLL_INTERVAL = 120  # 2 minutes
TEAMS_API_KEY = "your-api-key"
ANTHROPIC_API_KEY = "your-anthropic-key"

def get_new_messages(last_check_time):
    """Poll Teams API for new messages since last check."""
    response = requests.get(
        "https://graph.microsoft.com/v1.0/me/messages",
        headers={"Authorization": f"Bearer {TEAMS_API_KEY}"},
        params={"$filter": f"receivedDateTime ge {last_check_time}"}
    )
    return response.json().get("value", [])

def generate_ai_response(message_content):
    """Use Claude to generate a response."""
    client = Anthropic(api_key=ANTHROPIC_API_KEY)
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": message_content}]
    )
    return response.content[0].text

def send_teams_reply(message_id, reply_content):
    """Send reply back to Teams."""
    # Implementation varies by platform
    pass

# Main polling loop
last_check = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())

while True:
    try:
        messages = get_new_messages(last_check)

        for msg in messages:
            reply = generate_ai_response(msg["body"]["content"])
            send_teams_reply(msg["id"], reply)
            print(f"Processed message: {msg['subject']}")

        last_check = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())

    except Exception as e:
        print(f"Error during poll: {e}")
        # Continue polling despite errors

    time.sleep(POLL_INTERVAL)

Key observations:

Simple loop with try/catch
No server required
Errors don’t crash the system
Easy to debug and monitor

Now compare with a webhook implementation:

from flask import Flask, request, jsonify
import hmac
import hashlib
from anthropic import Anthropic

app = Flask(__name__)
CLIENT_SECRET = "your-webhook-secret"

def verify_webhook_signature(payload, signature):
    """Verify request is from Microsoft Teams."""
    expected = hmac.new(
        CLIENT_SECRET.encode(),
        payload,
        hashlib.sha256
    ).hexdigest()
    return hmac.compare_digest(expected, signature)

def generate_ai_response(message_content):
    """Use Claude to generate a response."""
    client = Anthropic(api_key="your-anthropic-key")
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": message_content}]
    )
    return response.content[0].text

@app.route("/webhook/teams", methods=["POST"])
def handle_teams_webhook():
    """Handle incoming Teams webhook."""
    # 1. Verify signature
    signature = request.headers.get("X-Microsoft-Signature")
    if not verify_webhook_signature(request.data, signature):
        return jsonify({"error": "Invalid signature"}), 401

    # 2. Parse event
    event = request.json

    # 3. Handle validation handshake
    if event.get("type") == "validation":
        return jsonify({"validationToken": event.get("validationToken")})

    # 4. Process message
    if event.get("type") == "message":
        message_content = event["body"]["content"]
        reply = generate_ai_response(message_content)
        send_teams_reply(event["id"], reply)
        return jsonify({"status": "processed"})

    return jsonify({"status": "ignored"})

if __name__ == "__main__":
    # Requires HTTPS for webhooks
    app.run(ssl_context="adhoc", port=443)

The webhook code isn’t dramatically longer. But this doesn’t include:

Azure AD setup:

Register app in Azure Portal
Configure redirect URIs
Request Microsoft Graph permissions
Create webhook subscription
Handle OAuth token refresh

Each step involves platform-specific complexity, documentation hunting, and debugging. The “proper” approach multiplies effort.

The Hidden Costs of “Smart” Architecture

When people argue for webhooks, they often ignore developer time as a resource.

Building webhook infrastructure realistically takes:

4-8 hours: Azure AD registration, permission configuration, troubleshooting access issues
2-4 hours: Setting up SSL, server, public endpoint, DNS
4-8 hours: OAuth implementation, token refresh, error handling, retry logic
Ongoing: Security patches, certificate renewals, server monitoring

For my personal tool:

Polling: 720 API calls/day (mostly empty responses)
Webhook: ~20 hours setup + ongoing maintenance

At typical cloud API rates, those empty polling calls cost fractions of a penny. Developer time costs orders of magnitude more.

This isn’t an argument for laziness. It’s an argument for pragmatism. The “right” architecture depends on context.

Comparison Table: When to Use What

| Aspect                | Polling                          | Webhooks                    |
|-----------------------|----------------------------------|-----------------------------|
| Setup Complexity      | Low - just a loop                | High - auth, permissions    |
| Initial Dev Time      | Hours                            | Days                        |
| Latency               | Average: polling interval / 2    | Near instant                |
| Resource Efficiency   | Low - constant requests          | High - only when needed     |
| Infrastructure        | None (can run locally)           | Public server required      |
| Auth Complexity       | Simple (API key in request)      | Complex (OAuth, signatures) |
| Error Handling        | Retry on next poll               | Immediate handling needed   |
| Scalability           | Poor (more users = more polls)   | Excellent (event-driven)    |
| Debugging             | Easy (pull-based)                | Harder (push-based)         |
| Cost at Scale         | Higher (constant API calls)      | Lower (event-driven)        |
| Best For              | Prototypes, personal, low-volume | Production, enterprise      |

Decision Framework

Use this decision tree:

START
  │
  ├─ Need real-time response (<1 second)?
  │     └─ YES → Webhooks
  │     └─ NO ↓
  │
  ├─ Processing >1000 messages/day?
  │     └─ YES → Webhooks
  │     └─ NO ↓
  │
  ├─ Have public server with HTTPS?
  │     └─ NO → Polling
  │     └─ YES ↓
  │
  ├─ Building for enterprise/compliance?
  │     └─ YES → Webhooks
  │     └─ NO ↓
  │
  ├─ Limited dev time (<1 day)?
  │     └─ YES → Polling
  │     └─ NO ↓
  │
  └─ Consider hybrid approach

Hybrid Approach: Best of Both Worlds

For those wanting efficiency without webhook complexity, consider adaptive polling:

def smart_poll():
    interval = 60  # Start at 1 minute
    max_interval = 300  # Max 5 minutes

    while True:
        messages = get_new_messages()

        if messages:
            process_messages(messages)
            interval = 60  # Reset to fast polling when active
        else:
            # No activity - slow down progressively
            interval = min(interval * 1.5, max_interval)

        time.sleep(interval)

Benefits:

Near real-time during active conversations
Efficient during quiet periods
No webhook infrastructure required
Simple to implement and maintain

This approach gives you most of the benefits of webhooks without the infrastructure overhead.

What I Learned

After running my “dumb” polling solution for months, I’ve processed thousands of Teams messages with zero issues. The 1-minute average latency hasn’t mattered for my use case.

The Reddit critics weren’t wrong about efficiency. Webhooks are more resource-efficient at scale. But they missed the bigger picture: developer efficiency matters too.

For prototypes, personal tools, and low-volume integrations, polling is the pragmatic choice. You can always refactor to webhooks when scale demands it—and you’ll have a working system that taught you the domain while you built it.

Start simple. Measure. Optimize when necessary. The “dumb” solution that ships today beats the “smart” solution that’s still in planning.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: I made Claude respond to my Microsoft Teams messages
👨‍💻 Microsoft Graph API Webhooks Documentation
👨‍💻 Slack Events API
👨‍💻 Discord Webhooks Guide
👨‍💻 Anthropic Claude API Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!