3 Patterns for Multi-Agent Coordination That Actually Work

After watching hundreds of multi-agent sessions, these are the coordination patterns that consistently prevent conflicts and wasted tokens.

Why Coordination Matters

Running multiple AI agents without coordination is like having three chefs in a kitchen with no communication. They'll each try to make the soup, argue over the salt, and nobody makes dessert.

Here are three patterns we've seen work consistently across real projects.

Pattern 1: Announce Before You Act

The simplest and most effective pattern. Before an agent starts a task, it tells everyone what it's about to do:

# Step 1: Check what's happening
→ hivemind_status()
  Active Tasks: 1 (Adding rate limiting — cursor-agent)
  Blockers: 0
# Step 2: Announce your task
→ hivemind_publish(
    channel: "backend",
    event_type: "task.created",
    data: { description: "Refactoring auth middleware to use JWT" }
  )
✓ Published — no conflicts detected
# Step 3: Do the work
...
# Step 4: Announce completion
→ hivemind_publish(
    event_type: "task.completed",
    data: {
      description: "Auth middleware refactored",
      files_changed: ["src/auth.ts", "src/middleware.ts"]
    }
  )

Other agents query the log before starting their own work and avoid stepping on active tasks. The overhead is one tool call at the start, one at the end. The payoff is zero duplicate work.

Pattern 2: Lock, Edit, Release

For file-level coordination, advisory locks prevent two agents from editing the same file simultaneously:

# Acquire the lock
→ hivemind_lock(action: "acquire", resource: "src/auth.ts")
✓ locked: true
# Edit the file safely
... editing src/auth.ts ...
# Release when done
→ hivemind_lock(action: "release", resource: "src/auth.ts")
✓ released

What happens when another agent tries to lock the same file?

→ hivemind_lock(action: "acquire", resource: "src/auth.ts")
⚠ locked: false
  held_by: claude-agent-1
  since: 2 minutes ago
# Agent pivots to a different task instead

The lock isn't hard — it's advisory. But agents respect it, and the result is zero merge conflicts on shared files.

Pattern 3: Decision Trail

When an agent makes an architectural decision, it publishes the *reasoning* — not just the outcome:

→ hivemind_publish(
    channel: "architecture",
    event_type: "decision.made",
    data: {
      description: "Using Convex instead of Supabase",
      reasoning: "Need real-time subscriptions and
                  serverless functions in one platform"
    }
  )

Later, when another agent is about to evaluate the same decision, it finds the answer instantly:

→ hivemind_query(query: "database decision")
Found 1 result (0.92 similarity):
  decision.made · architecture · 3h ago
  "Using Convex instead of Supabase"
  Reasoning: Need real-time subscriptions and
  serverless functions in one platform

Without this, the second agent would spend 5-10 minutes re-evaluating the same decision. Multiply that by every decision across every agent, and the savings are massive.

Combining Patterns

The most effective setups combine all three:

The overhead is minimal — a few extra tool calls per task. The reduction in conflicts and wasted work is dramatic. We've seen teams go from 30%+ conflict rates to near zero with these three patterns.