Leaked Claude Code Deep Dive: Multi-Agent Control Planes
Multi-agent isn’t “run 5 prompts in parallel.” In production, it’s orchestration: routing messages, delegating permissions, isolating execution, and keeping the system debuggable. This post summarizes patterns into a reusable control-plane blueprint.
Leader/Worker delegation
A leader handles user intent, delegates scoped work to workers, and merges results. This is the cleanest way to scale an agent beyond one context window.
- Scoped prompts per worker
- Synthesis step owned by leader
- Clear “who decides” boundaries
Structured messaging (mailbox protocol)
Don’t rely on “whatever appears in the UI.” Use explicit messages that carry summaries, payloads, and status markers.
- Direct, broadcast, and routed messages
- Durable transport (file mailbox is fine)
- Locking to avoid race conditions
Centralized permissions
Multi-agent multiplies risk. Route dangerous actions to a single control plane (often the leader) for approval.
- Fail-closed defaults
- Permission requests include tool + input
- Leader-approved execution path
Multiple execution backends
Different environments want different trade-offs: speed (in-process) vs visibility/isolation (pane-based).
- In-process isolation
- Pane-based workers (tmux/iTerm) for visibility
- Graceful detection + fallback
Worktree isolation for experiments
Let agents experiment in isolated git worktrees. Keep them if changed, auto-clean if empty.
- Separate branch + working directory
- Easy cleanup for failed attempts
- Better safety for refactors
// Example: coordinator allowed tools
const COORDINATOR_ALLOWED = ['spawnWorker', 'sendMessage', 'stopTask'];
function canUseTool(agentRole: 'coordinator' | 'worker', toolName: string) {
if (agentRole === 'coordinator') return COORDINATOR_ALLOWED.includes(toolName);
return true; // workers are scoped by permissions + allowed paths
}Scaling agent teams inside the enterprise?
Elatify helps you implement safe multi-agent orchestration with governance, observability, and control.