Wanna try creating > AI agents that work smoothly and can communicate easily with other agents?

The Mastra AI TypeScript framework and the Agent-to-Agent (A2A) protocol make that possible. If you’re new to A2A, read this overview — it’s the protocol that allows different agents to talk, share data, and work together intelligently.

🧠 What is Mastra AI?

Mastra is a TypeScript framework for building AI agents that can reason, act, and collaborate. It helps developers create structured, modular, and scalable agents capable of performing specific tasks or communicating with other systems.

Some of Mastra’s key strengths include:

Smooth TypeScript integration

Built-in multi-agent communication via A2A

Flexible plugin-based design

Easy deployment across different environments

You can think of Mastra as the bridge between AI logic and real-world integrations.

A Walkthrough of how i implemented my debug assitant agent When I first created a new Mastra project using their CLI, I noticed something interesting — it came with a fully functional Weather Agent right out of the box. All I had to do was plug in my AI API key, and it started responding immediately.

That made understanding how Mastra agents work incredibly easy. I simply studied how the built-in weather agent was structured — how it handled input, produced responses, and interacted through the A2A protocol — and then began adapting that pattern to my own use case. Check here for more info on mastra ai setup

💡 Introducing Debuggify

I decided to build Debuggify, an AI-powered debugging agent. Its job is simple but powerful — you paste an error message, and Debuggify:

Explains what the error means,

Suggests possible causes and solutions, and

Sends you links to relevant documentation or Stack Overflow discussions for deeper understanding.

It’s meant to feel like having a friendly, knowledgeable dev partner that doesn’t just fix problems but helps you understand them.

🧩 Connecting Debuggify to Telex

Once I had Debuggify working locally, the next step was making it accessible and able to communicate with other systems. Instead of using an API key or direct SDK integration, I connected it through a webhook endpoint that linked my deployed Mastra agent (hosted on Mastra Cloud) with Telex and all communicating via the A2A protocol.

This webhook/A2A setup acted as a communication bridge — whenever Telex received a message or request, it passed it directly to Debuggify, which then processed the input and sent back a structured, A2A compliant AI-generated response.

The process was smooth because Mastra’s built-in A2A structure already handled message formatting and routing. All I really had to do was define how Debuggify interprets requests and crafts its replies.

⚙️ The Integration Flow

Here’s a quick overview of the flow I built:

A user or agent sends an error message via Telex.

Telex forwards that message to my Debuggify webhook endpoint.

Debuggify analyzes the error and generates a structured response — explanation, possible cause, fix suggestions, and related resources.

The response is sent back to Telex and displayed neatly for the user or another agent.

This setup makes Debuggify not just a standalone helper but a collaborative agent that can slot into any workflow — especially useful when other agents need debugging insights on the fly.

🚀 Wrapping It Up

Building Debuggify with Mastra and integrating it through Telex showed me just how much potential there is in this new agent ecosystem. Mastra handled the complex parts — reasoning, interaction, and deployment — while Telex made it easy to connect my agent to the real world through a lightweight webhook and A2A.

It’s a small step toward a bigger goal: agents that can talk, learn, and work together seamlessly.