u/Flimsy_Pumpkin6873

Is there a place where I can read real use cases / actual deployments of AI Agents in real scenarios?

The internet is flooded with examples similar to below but these in my head are not true AI Agents right?

1. If email arrives with pdf, check pdf for invoice information and put it in a google sheet is not a AI Agent? Its a workflow that now has llm call as a node

2. Check my google search console and suggest ideas for SEO - This again is a cron job (run every xhrs), collate information and feed it into a llm to generate ideas. This is a workflow as well.

3. personal assistants - I ask for information and llm figures out which tool to call and gets it and writes to a database

perhaps coding agents which do some stuff autonoumously when prompted is a good example. Is there a compilation of real use case anywhere online?

Hi All,

I made a MCP server (Gavi Whatsapp) that enables you to connect your agents to your whatsapp business accounts easily and send whatsapp messages. It exposes these tools:

Tool	What it does
send_message	Send free-form text within the 24-hour conversation window
send_template	Send a Meta-approved template (cold messages, OTPs, transactional, marketing)
send_media	Send image, video, document, audio
send_broadcast	Send a template to many recipients with per-row variable substitution
list_templates	Discover available approved templates
list_messages	Read message history with delivery status

Would love to get your feedback and understand if your pain points in connecting your ai agents to whatsapp numbers and send messages.

I was struggling to figure out how to connect my replit project to whatsapp and found this great article on how to go about it.. just sharing incase you find it helpful ..
https://www.gaviventures.com/blog/whatsapp-replit-5-min

If you've ever tried to wire WhatsApp into a side project, you know the Meta Cloud API onboarding is rough:

- Create a Meta Business account
- Verify the business (some countries take days)
- Provision a phone number, then verify it via OTP
- Create a WhatsApp Business Account (WABA) and link it
- Generate a system-user access token (don't use the temporary one — it
expires every 24h)
- Read 6 different doc pages to figure out template categories
- Submit a template for approval (24-48h wait)
- Set up a webhook with HMAC verification
- Then write the actual HTTP calls

Thats complex and so I built a api wrapper around it. You can now setup easily with the following steps:

1. npm install gaviwhatsapp/whatsapp
2. Add GAVIVENTURES_API_KEY=gv_... to your env
3. Three lines of code:
4. import { WhatsApp } from '@gaviwhatsapp/whatsapp'
5. const wa = new WhatsApp({ apiKey: process.env.GAVIVENTURES_API_KEY })
6. await wa.send({ to: '+919876543210', text: 'Hello!' })

(Python SDK and a REST API are also there if you don't use Node.)

Pricing:
- Message costs go directly to your Meta Business Account at Meta's published rates — no per-message markup from us
- Flat $9.99/mo for the API+SDK+MCP

Bonus: there's an MCP server (@gaviwhatsapp/mcp) so Cursor / Claude Code
/ Codex / AI Agents can send WhatsApp directly during a coding session. Useful when
you want the agent to actually verify the integration works at the end of
a feature.

What I'd love feedback on:
- Suggestions on actual pain points and what can i build to improve this?
- For folks who picked Twilio/Wati/MessageBird/etc., what made you pick those
over rolling your own integration with Meta Cloud API?

API docs at: https://www.gaviventures.com/docs/api (the Cursor / Claude/ Codex skill files are there too)

If you've ever tried to wire WhatsApp into a side project, you know the Meta Cloud API onboarding is rough:

- Create a Meta Business account
- Verify the business (some countries take days)
- Provision a phone number, then verify it via OTP
- Create a WhatsApp Business Account (WABA) and link it
- Generate a system-user access token (don't use the temporary one — it
expires every 24h)
- Read 6 different doc pages to figure out template categories
- Submit a template for approval (24-48h wait)
- Set up a webhook with HMAC verification
- Then write the actual HTTP calls

That's a half day on a good day. Most people give up by step 5, So I built a wrapper around it. Setup is now:

1. npm install gaviwhatsapp/whatsapp
2. Add GAVIVENTURES_API_KEY=gv_... to your env
3. Three lines of code:
4. import { WhatsApp } from '@gaviwhatsapp/whatsapp'
5. const wa = new WhatsApp({ apiKey: process.env.GAVIVENTURES_API_KEY })
6. await wa.send({ to: '+919876543210', text: 'Hello!' })

(Python SDK and a REST API are also there if you don't use Node.)

Where the 5 minutes actually goes:
- ~30 sec: signup at gaviventures.com
- ~2 min: connecting your WABA (if you already have one) or creating one
through the dashboard wizard (if you don't, this part still takes time for Meta to verify, but you can integrate the SDK in parallel)
- ~30 sec: paste API key, npm install
- ~1 min: write 3 lines, send a test message to your own phone

What's behind the curtain:
- Message costs go directly to your Meta Business Account at Meta's published rates — no per-message markup from us
- Flat $9.99/mo for the API+SDK+MCP

Bonus: there's an MCP server (@gaviwhatsapp/mcp) so Cursor / Claude Code
/ Codex / AI Agents can send WhatsApp directly during a coding session. Useful when
you want the agent to actually verify the integration works at the end of
a feature.

What I'd love feedback on:
- Is the 3-line API actually clean, or am I oversimplifying?
- Suggestions on actual pain points and what can i build to improve this?
- For folks who picked Twilio/MessageBird/etc., what made you pick those
over rolling your own integration with Meta Cloud API?

API docs at: https://www.gaviventures.com/docs/api (the Cursor / Claude/ Codex skill files are there too)