🚀 How I Built an AI Agent with Multi-Channel Input + WhatsApp Integration (and Solved a Real Bottleneck)

Recently, I built an **AI agent** that collects necessary information from users (via **audio, video, or text**) and then **triggers an email to the manager** requesting a quotation. The idea was to streamline client interactions and make the communication seamless.

💬 Client Request: WhatsApp Integration

The client wanted the AI agent to also work over **WhatsApp**, so that users could chat directly. I integrated this using **Twilio Webhooks** — incoming WhatsApp messages were routed to the backend, processed, and then the agent responded.

⚡ The Problem

During testing, I noticed a major issue:

- If a user sent multiple messages quickly (e.g., multiple texts or images), the webhook would forward them individually.

- Since my backend was processing messages one at a time (effectively single-threaded per user), **any new messages arriving while processing was still happening were ignored**.

- This broke the experience — especially when users uploaded several images or fired off multiple texts in a row.

🧠 The Fix: Redis Message Buffering

To solve this, I added **Redis as a temporary message queue**:

- Every incoming message was stored in Redis.

- Instead of processing immediately, the system would **wait 5 seconds**.

- During that window, if multiple messages came in (text + images, etc.), they were grouped together.

- After 5 seconds, **all messages were processed at once** and sent as a single batch to the backend.

✅ The Result

- Users could now send multiple messages naturally without anything being dropped.

- The agent received a **complete context** (all related messages/images together).

- **Cost efficiency improved** since instead of firing multiple API calls for each message, one batched call was made per user session.

- The whole interaction flow became **smoother and more reliable**.

🌟 This small change made a big difference in both **user experience** and **system efficiency**.

It’s a reminder that sometimes the best optimizations come from watching how **real users interact** and designing around those patterns.

👉 Curious to hear your thoughts: Was this the right approach, or could there have been a better solution?

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