Build when you sleep - Claude code + Openclaw +Telegram +EC2 + tmux | Context Engineering | Lec 7

Описание: Want to go beyond just watching? Enroll in the Engineer Plan or Industry Professional Plan at
https://context-engineering.vizuara.ai

These plans give you access to Google Colab notebooks, interactive exercises, private Discord community, Miro boards, a private GitHub repository with all code, and capstone build sessions where you build production-grade AI agents alongside the instructors. Everything is designed so that you can actually implement what you learn, not just watch it.

Enroll here: https://context-engineering.vizuara.ai

In Session 7 of the AI Context Engineering Bootcamp, Dr. Sreedath Panat walks through how to set up a real, production-style workflow for AI agents, moving from concepts to actual system design and deployment.

The session starts with a practical overview of the tools required to run AI agents in a real environment. As shown in the lecture slides, the stack includes Claude Code, CCO for sandboxed execution, AWS EC2 for remote compute, VS Code Remote SSH for development, OpenClaw for remote agent access, Telegram for interaction, and tmux for managing persistent terminal sessions.

We then move into how to structure development workflows using sprints and task decomposition. Instead of working in an unstructured way, the system is organized into sprint folders, where each sprint is broken down into small atomic tasks. Custom commands such as /prd help define requirements, /dev focuses on implementation with test-driven development, and /walkthrough generates readable summaries of what has been built.

A key part of the lecture explains how tmux works as a backbone for long-running AI systems. It allows you to manage multiple terminal sessions inside a single window, ensuring that agents continue running even if your local machine disconnects.

We also explore why Telegram is used as an interface layer. Telegram offers a bot-first design with well-documented APIs, allowing agents to send and receive messages programmatically without friction. This makes it a clean and fast way to interact with remote AI systems compared to more restrictive platforms.

The session then introduces CCO (Claude Code Orchestrator), which provides a sandboxed environment where AI coding agents can operate safely without exposing your full system. This ensures a balance between autonomy and security.

To enable remote execution, we discuss SSH and .pem files, explaining how secure connections to cloud machines such as EC2 instances are established. This leads into a clear explanation of AWS EC2, where virtual machines are used to run AI workloads without needing physical hardware.

Finally, we look at OpenClaw as a remote access layer, which allows you to control Claude Code from interfaces like Telegram or a browser instead of being tied to a terminal session. This completes the full loop from local development to remote, production-style AI agent orchestration.

By the end of this session, you will have a clear understanding of how to set up, manage, and interact with AI agents in a real-world workflow that goes far beyond simple notebook experiments.