Mesh Architecture for AI Agents: Client Isolation and Cross-Project Coordination
Mesh vs. Hub-and-Spoke Architecture
The author contrasts their mesh approach with the hub-and-spoke model popularized by setups like Daniel Miessler's PAI. In hub-and-spoke, one central assistant with shared memory handles all workflows, which trades depth for breadth. The mesh architecture creates domain-specialist agents for each client's project, with each agent carrying deep project context without competing for memory space.
System Implementation Details
The system uses plain markdown files and naming conventions for coordination:
STATE.mdfor working memoryCLAUDE.mdandAGENTS.mdfor agent instructions- Structured memos for cross-project communication
- Git for version control underneath everything
There's no database, no platform, and no dependencies beyond CLI tools. Each project is its own node with its own state and instruction files, ensuring Client A's context is isolated from Client B's session.
Cross-Agent Communication
Agents coordinate by dropping structured memos (plain markdown files) into each other's incoming directories, similar to emails passing between team members. Examples include:
- A content agent finishing a draft that the developer agent picks up next session
- A sysadmin agent finding a bug and sending it to the web dev agent
- Infrastructure changes affecting websites
- Content decisions depending on project management input
- Requirements specs triggering development work
For projects needing true isolation where SSH access would defeat the purpose, the memo system extends to email so there's no direct access between environments.
Tool Agnostic Approach
The author uses Claude Code, Codex, Gemini CLI, and DeepAgent across different projects. Because the conventions are just files, there isn't a noticeable vendor boundary—a Claude agent can send a memo that a Codex agent picks up. Swapping vendors to meet project needs is a standard part of the workflow.
Practical Results
This system has been running on real client work for about a year, handling 44 projects across 14 organizations. The author previously carried all coordination between agents but now just reviews work instead of passing it along.
📖 Read the full source: r/ClaudeAI
👀 See Also
How Letting OpenClaw Improve Its Own Environment Creates Sustainable Workspaces
An experienced OpenClaw user found the biggest productivity gain came from allowing the agent to update its own internal docs, edit operating files, refine prompts, build custom tools, write scripts, and document lessons. Their workspace structure includes key markdown files like SOUL.md for behavioral style, AGENTS.md for operational conventions, and MEMORY.md as a lightweight index.
Developer Builds 6 Claude AI Agents to Manage 15 Side Projects
A developer with a full-time engineering job created six specialized Claude agents to handle daily operations for 15 side projects, using Claude Code, markdown files, and git worktrees without a custom platform.
Reducing Voice Command Friction for Telegram AI Agent with iOS Back Tap
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Developer uses Claude Code to iterate spending chart from wireframe to production quality in one night
A developer building a personal finance app solo used Claude Code to redesign a spending chart through four rounds of fixes in a single session, going from basic wireframe to near-production quality in about 3 hours.