Context Windows in AI Coding Agents: Sandboxes, Memory, and Routing

Context Windows in AI Coding Agents: Sandboxes, Memory, and Routing

AI agents struggle with context windows. They hit limits fast and drop key details. The mksglu/context-mode repo solves this for coding agents that touch multiple platforms.

It keeps tool output small through sandboxes. It holds session memory across calls. It routes requests to the right backend for 17 platforms.

The Setup

Coding agents need to call tools without filling the context. A sandbox limits what the tool can do. This stops runaway processes or data leaks.

Session memory stores what happened before. It lets the agent remember previous decisions without re-sending everything.

Routing decides where to send each request. Direct for local, mcp for remote, or platform specific for android or ios.

# Example routing for platforms
def route_to_backend(request, platform):
    if platform in ['android', 'ios', 'mac']:
        return 'mcp_server'
    return 'direct_engine'

The Numbers

Tool output drops 98 percent with the sandbox. The agent keeps running without blowing up memory.

This matters for long sessions. Without it, agents hit context limits and fail.

Lessons

Architecture beats model size here. Good memory and routing keep agents useful.

Pick the right backend per platform. Test the sandbox early.

The repo shows how context mode turns raw agent power into reliable, platform-aware code review.

Check the repo for full code. It runs on standard setups.

Start small. Add more platforms as needed.

The approach works for real coding tasks. Agents stay in control.