inter-agent-messaging

stratum	2
name	inter-agent-messaging
description	Filesystem-based message passing for agent communication. Use when user asks about agent messaging, inbox/outbox, how agents talk to each other, multi-agent workflows, agent coordination, passing data between agents, agent handoffs, or working around the subagent spawn constraint. Also triggers on "how do I use messaging", "send message to agent", "agent communication".
branches	["agentic"]

Inter-Agent Messaging

Quick Start - How to Use This

Check inbox status:

/workflow-dispatch status

Send a message to an agent:

/workflow-dispatch send skill-writer "Create a skill for Python testing"

Process pending messages:

/workflow-dispatch poll

That's it! The /workflow-dispatch command handles everything. See below for how it works.

---

Purpose

Defines a filesystem-based message passing pattern for agent communication in Claude Code, where agents CANNOT spawn other agents directly.

---

Critical: Same Agents, Communication Layer

This pattern does NOT create new agents. It adds a communication layer for EXISTING agents.

Agent (TAB/SHIFT+TAB)	Its Mailbox
skill-writer	.claude/inboxes/skill-writer/
explore	.claude/inboxes/explore/

The inbox is WHERE an agent looks for work. The agent is still spawned via Task tool.

Agents don't "watch" their inboxes - the orchestrator polls and spawns them.

---

Quick Reference

Concept	Rule
Inboxes	Capture area where messages enter for an agent
Outboxes	Output area where completed responses go
Shared	Work area for shared state/context
Status	pending -> in_progress -> completed
Orchestrator	Command or main context that polls and dispatches

---

Why This Pattern?

In Claude Code, agents cannot spawn other agents. This creates a coordination problem:

Agent A needs Agent B's analysis
      |
      v
Agent A CANNOT invoke Agent B
      |
      v
??? How do they communicate ???

Solution: Filesystem-based message passing with an orchestrating layer.

Agent A writes request -> Inbox for B
         |
Orchestrator polls -> sees pending message
         |
Orchestrator spawns Agent B -> reads inbox
         |
Agent B writes response -> Outbox
         |
Orchestrator routes -> back to Agent A or next step

---

Folder Structure (SEACOW Aligned)

.claude/
├── inboxes/                    # CAPTURE - messages enter here
│   ├── skill-writer/           # Inbox per agent
│   │   └── 2025-01-01-001.md
│   ├── explore/
│   └── workflow-expert/
│
├── outboxes/                   # OUTPUT - completed responses
│   ├── skill-writer/
│   │   └── 2025-01-01-001-response.md
│   └── explore/
│
└── shared/                     # WORK - shared context/state
    ├── session-context.md      # Current session state
    └── workflow-state.md       # Multi-step workflow state

SEACOW Mapping

Folder	SEACOW Activity	Purpose
inboxes/	Capture	Where requests enter the system
outboxes/	Output	Where responses exit to consumers
shared/	Work	Where shared state is processed

---

Message Format

Request Message (Inbox)

---
id: 2025-01-01-001
from: explore
to: skill-writer
priority: normal           # low | normal | high | urgent
status: pending            # pending | in_progress | completed | failed
created: 2025-01-01T12:00:00Z
context: "Creating skill from exploration findings"
workflow: create-skill-from-research
step: 2
---

## Request

Create a new skill based on the following exploration findings.

## Payload

[Content from previous agent or user]

## Expected Output

- Skill file created at appropriate location
- Summary of what was created

## References

- `.claude/outboxes/explore/2025-01-01-001-response.md`

Response Message (Outbox)

---
id: 2025-01-01-001-response
request_id: 2025-01-01-001
from: skill-writer
to: explore
status: completed
created: 2025-01-01T12:05:00Z
duration_seconds: 45
---

## Summary

Created skill `python-testing` at `.claude/skills/python-testing/SKILL.md`.

## Result

[Detailed output]

## Files Created

- `/path/to/skill/SKILL.md`

## Recommendations

[Next steps if any]

---

Frontmatter Schema

Required Fields

Field	Type	Description
id	string	Unique identifier (timestamp + sequence)
from	string	Sending agent name
to	string	Receiving agent name
status	enum	pending, in_progress, completed, failed
created	ISO8601	Creation timestamp

Optional Fields

Field	Type	Description
priority	enum	low, normal, high, urgent
context	string	Brief context for the request
workflow	string	Parent workflow identifier
step	number	Step number in workflow
request_id	string	(responses) Links to original request
duration_seconds	number	(responses) Processing time
error	string	(failed) Error description

---

Orchestration Pattern

The orchestrator (command or main context) manages the message flow:

Polling Logic

## Orchestrator Poll Cycle

1. **Scan inboxes** for `status: pending` messages
2. **Sort by priority** (urgent > high > normal > low)
3. **For each pending message:**
   a. Update status to `in_progress`
   b. Spawn the target agent with message content
   c. Agent reads inbox, processes, writes to outbox
   d. Update inbox status to `completed`
4. **Check outboxes** for completed responses
5. **Route responses** to next step or requesting agent

Command Implementation Example

## /workflow-dispatch Command

1. Glob `.claude/inboxes/**/` for pending messages
2. Parse frontmatter, sort by priority
3. For highest priority pending:
   - Read message content
   - Spawn target agent with content as context
   - Wait for agent completion
   - Check outbox for response
   - Update statuses
4. Report results

---

Example Workflow

Scenario: Research -> Skill Creation

User: "Research Python testing patterns and create a skill"

Step 1: Command spawns Explore agent
        |
        v
Explore agent researches, writes to skill-writer inbox:
        .claude/inboxes/skill-writer/2025-01-01-001.md
        status: pending
        |
        v
Step 2: Command sees pending message
        Command spawns skill-writer agent
        |
        v
Skill-writer reads inbox, creates skill, writes to outbox:
        .claude/outboxes/skill-writer/2025-01-01-001-response.md
        status: completed
        |
        v
Step 3: Command reads outbox, reports to user

Message Flow Diagram

User Request
    |
    v
+-------------------+
|   COMMAND         |  (Orchestrator - has conversation context)
+-------------------+
    |
    | spawns
    v
+-------------------+       writes       +---------------------------+
|   EXPLORE AGENT   | -----------------> | inboxes/skill-writer/     |
+-------------------+                    | 2025-01-01-001.md         |
    |                                    | status: pending           |
    | returns                            +---------------------------+
    v                                              |
+-------------------+                              |
|   COMMAND         |  polls, sees pending         |
+-------------------+ <----------------------------+
    |
    | spawns
    v
+-------------------+       reads        +---------------------------+
|  SKILL-WRITER     | <----------------- | inboxes/skill-writer/     |
|     AGENT         |                    | 2025-01-01-001.md         |
+-------------------+                    +---------------------------+
    |
    | writes
    v
+-------------------+                    +---------------------------+
| outboxes/         | -----------------> | skill-writer/             |
| skill-writer/     |                    | 2025-01-01-001-response.md|
+-------------------+                    | status: completed         |
    |                                    +---------------------------+
    | returns
    v
+-------------------+
|   COMMAND         |  reads outbox, reports to user
+-------------------+

---

Shared State Pattern

For workflows needing shared context across agents:

Session Context

# .claude/shared/session-context.md
---
session_id: 2025-01-01-session-001
started: 2025-01-01T10:00:00Z
user_goal: "Set up Python testing infrastructure"
agents_invoked:
  - explore (completed)
  - skill-writer (in_progress)
---

## Current State

[Accumulated context from all agents]

## Decisions Made

- Using pytest over unittest
- Fixtures for database mocking

## Open Questions

- Coverage threshold?

Workflow State

# .claude/shared/workflow-state.md
---
workflow_id: create-skill-from-research
status: in_progress
current_step: 2
total_steps: 3
---

## Steps

1. [x] Explore: Research Python testing patterns
2. [ ] Skill-Writer: Create skill from findings
3. [ ] Validator: Test skill activation

## Accumulated Results

[Summary from each completed step]

---

Implementation Checklist

Setting Up

1. Create folder structure:

   mkdir -p .claude/inboxes .claude/outboxes .claude/shared
   

2. Create agent-specific inboxes as needed:

   mkdir -p .claude/inboxes/skill-writer
   mkdir -p .claude/inboxes/explore
   

3. Add to .gitignore (optional - messages are ephemeral):

   .claude/inboxes/
   .claude/outboxes/
   .claude/shared/
   

Agent Integration

Agents that participate in messaging should:

1. Check inbox on start (if designed to receive messages)
2. Write to target inbox when needing another agent
3. Write to outbox when completing a request
4. Update status fields appropriately

Orchestrator Commands

Create commands that:

1. Poll inboxes for pending messages
2. Dispatch to appropriate agents
3. Track workflow state
4. Clean up completed messages

---

Anti-Patterns

Don't	Do Instead
Expect agents to read each other's outboxes directly	Route through orchestrator
Skip status updates	Always update status field
Use messages for real-time communication	Use for async handoffs only
Store sensitive data in messages	Keep credentials in secure config
Let messages accumulate	Clean up completed workflows
Assume message order	Use explicit step numbers

---

When to Use This Pattern

Good Use Cases:

• Multi-agent workflows with distinct phases
• Long-running processes with handoffs
• Workflows where one agent's output feeds another
• Audit trails of agent interactions

Not Needed When:

• Single agent can handle the task
• Simple exploration without handoffs
• Direct user interaction (no agent-to-agent)

---

Related Skills

• agent-patterns -- How to design agents that use messaging
• seacow-conventions -- SEACOW framework underlying the folder structure
• workflow-guide -- How workflows compose in this scaffold