Reducing AI Agent Context Bloat with Single Workspace Architecture

A developer on r/openclaw detailed their approach to reducing AI agent context bloat by moving from complex "agent swarms" to a single workspace architecture. They reported cutting startup context from 27,000 tokens to 4,000 tokens (85% reduction) after implementing several specific changes.

Key Implementation Details

The approach involved four concrete modifications:

• Gut the Root Config: Stripped the global AGENTS.md file down to only bare essentials (voice and universal rules), acting purely as a baseline. Completely deleted the global MEMORY.md file.
• Channel-Level Identity Injection: Hard-coded project isolation into the chat environment by mapping specific Discord channels to specific project environments using OpenClaw. Example configuration:

"1478382862150664344": {
  "systemPrompt": "You are the social media agent in #social-media. Focus exclusively on LinkedIn-to-Substack growth. Stay in the memory/social_media/ folder.\nStartup: read memory/social_media/YYYY-MM-DD.md (today) and memory/social_media/MEMORY.md.",
  "skills": ["linkedin-content-writing", "nano-banana-pro"]
}

• Segregated Memory Folders: Each channel gets its own dedicated folder (e.g., memory/social_media/) containing the channel's daily working log (YYYY-MM-DD.md) and the channel's own separated, project-specific MEMORY.md file.
• Slicing the Tool Tax: Moved to a minimal global tool profile and injected specialized skills only when the agent is in the relevant channel, as shown in the "skills" array in the configuration.

The developer noted that before these changes, their AI assistant spent 20 seconds reading its own context before responding, with context reaching 27,000 tokens across multiple projects. The new approach creates isolation in the agent's mind that matches the file system exactly.