Add Multi-Channel Customer Service and Autonomous PM use cases

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Oleh Veheria
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| Name | Description |
|------|-------------|
| [Autonomous Project Management](usecases/autonomous-project-management.md) | Coordinate multi-agent projects using STATE.yaml pattern — subagents work in parallel without orchestrator overhead. |
| [Multi-Channel AI Customer Service](usecases/multi-channel-customer-service.md) | Unify WhatsApp, Instagram, Email, and Google Reviews in one AI-powered inbox with 24/7 auto-responses. |
| [Phone-Based Personal Assistant](usecases/phone-based-personal-assistant.md) | Access your AI agent via phone calls, hands-free voice assistance for any phone. |
| [Inbox De-clutter](usecases/inbox-declutter.md) | Summarize Newsletters and send you a digest as an email. |
| [Personal CRM](usecases/personal-crm.md) | Automatically discover and track contacts from your email and calendar, with natural language queries. |

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# Autonomous Project Management with Subagents
Managing complex projects with multiple parallel workstreams is exhausting. You end up context-switching constantly, tracking status across tools, and manually coordinating handoffs.
This use case implements a decentralized project management pattern where subagents work autonomously on tasks, coordinating through shared state files rather than a central orchestrator.
## Pain Point
Traditional orchestrator patterns create bottlenecks—the main agent becomes a traffic cop. For complex projects (multi-repo refactors, research sprints, content pipelines), you need agents that can work in parallel without constant supervision.
## What It Does
- **Decentralized coordination**: Agents read/write to a shared `STATE.yaml` file
- **Parallel execution**: Multiple subagents work on independent tasks simultaneously
- **No orchestrator overhead**: Main session stays thin (CEO pattern—strategy only)
- **Self-documenting**: All task state persists in version-controlled files
## Core Pattern: STATE.yaml
Each project maintains a `STATE.yaml` file that serves as the single source of truth:
```yaml
# STATE.yaml - Project coordination file
project: website-redesign
updated: 2026-02-10T14:30:00Z
tasks:
- id: homepage-hero
status: in_progress
owner: pm-frontend
started: 2026-02-10T12:00:00Z
notes: "Working on responsive layout"
- id: api-auth
status: done
owner: pm-backend
completed: 2026-02-10T14:00:00Z
output: "src/api/auth.ts"
- id: content-migration
status: blocked
owner: pm-content
blocked_by: api-auth
notes: "Waiting for new endpoint schema"
next_actions:
- "pm-content: Resume migration now that api-auth is done"
- "pm-frontend: Review hero with design team"
```
## How It Works
1. **Main agent receives task** → spawns subagent with specific scope
2. **Subagent reads STATE.yaml** → finds its assigned tasks
3. **Subagent works autonomously** → updates STATE.yaml on progress
4. **Other agents poll STATE.yaml** → pick up unblocked work
5. **Main agent checks in periodically** → reviews state, adjusts priorities
## Skills You Need
- `sessions_spawn` / `sessions_send` for subagent management
- File system access for STATE.yaml
- Git for state versioning (optional but recommended)
## Setup: AGENTS.md Configuration
```text
## PM Delegation Pattern
Main session = coordinator ONLY. All execution goes to subagents.
Workflow:
1. New task arrives
2. Check PROJECT_REGISTRY.md for existing PM
3. If PM exists → sessions_send(label="pm-xxx", message="[task]")
4. If new project → sessions_spawn(label="pm-xxx", task="[task]")
5. PM executes, updates STATE.yaml, reports back
6. Main agent summarizes to user
Rules:
- Main session: 0-2 tool calls max (spawn/send only)
- PMs own their STATE.yaml files
- PMs can spawn sub-subagents for parallel subtasks
- All state changes committed to git
```
## Example: Spawning a PM
```text
User: "Refactor the auth module and update the docs"
Main agent:
1. Checks registry → no active pm-auth
2. Spawns: sessions_spawn(
label="pm-auth-refactor",
task="Refactor auth module, update docs. Track in STATE.yaml"
)
3. Responds: "Spawned pm-auth-refactor. I'll report back when done."
PM subagent:
1. Creates STATE.yaml with task breakdown
2. Works through tasks, updating status
3. Commits changes
4. Reports completion to main
```
## Key Insights
- **STATE.yaml > orchestrator**: File-based coordination scales better than message-passing
- **Git as audit log**: Commit STATE.yaml changes for full history
- **Label conventions matter**: Use `pm-{project}-{scope}` for easy tracking
- **Thin main session**: The less the main agent does, the faster it responds
## Based On
This pattern is inspired by [Nicholas Carlini's approach](https://nicholas.carlini.com/) to autonomous coding agents—let agents self-organize rather than micromanaging them.
## Related Links
- [OpenClaw Subagent Docs](https://github.com/openclaw/openclaw)
- [Anthropic: Building Effective Agents](https://www.anthropic.com/research/building-effective-agents)

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# Multi-Channel AI Customer Service Platform
Small businesses juggle WhatsApp, Instagram DMs, emails, and Google Reviews across multiple apps. Customers expect instant responses 24/7, but hiring staff for round-the-clock coverage is expensive.
This use case consolidates all customer touchpoints into a single AI-powered inbox that responds intelligently on your behalf.
## What It Does
- **Unified inbox**: WhatsApp Business, Instagram DMs, Gmail, and Google Reviews in one place
- **AI auto-responses**: Handles FAQs, appointment requests, and common inquiries automatically
- **Human handoff**: Escalates complex issues or flags them for review
- **Test mode**: Demo the system to clients without affecting real customers
- **Business context**: Trained on your services, pricing, and policies
## Real Business Example
At Futurist Systems, we deploy this for local service businesses (restaurants, clinics, salons). One restaurant reduced response time from 4+ hours to under 2 minutes, handling 80% of inquiries automatically.
## Skills You Need
- WhatsApp Business API integration
- Instagram Graph API (via Meta Business)
- `gog` CLI for Gmail
- Google Business Profile API for reviews
- Message routing logic in AGENTS.md
## How to Set It Up
1. **Connect channels** via OpenClaw config:
- WhatsApp Business API (through 360dialog or official API)
- Instagram via Meta Business Suite
- Gmail via `gog` OAuth
- Google Business Profile API token
2. **Create business knowledge base**:
- Services and pricing
- Business hours and location
- FAQ responses
- Escalation triggers (e.g., complaints, refund requests)
3. **Configure AGENTS.md** with routing logic:
```text
## Customer Service Mode
When receiving customer messages:
1. Identify channel (WhatsApp/Instagram/Email/Review)
2. Check if test mode is enabled for this client
3. Classify intent:
- FAQ → respond from knowledge base
- Appointment → check availability, confirm booking
- Complaint → flag for human review, acknowledge receipt
- Review → thank for feedback, address concerns
Response style:
- Friendly, professional, concise
- Match the customer's language (ES/EN/UA)
- Never invent information not in knowledge base
- Sign off with business name
Test mode:
- Prefix responses with [TEST]
- Log but don't send to real channels
```
4. **Set up heartbeat checks** for response monitoring:
```text
## Heartbeat: Customer Service Check
Every 30 minutes:
- Check for unanswered messages > 5 min old
- Alert if response queue is backing up
- Log daily response metrics
```
## Key Insights
- **Language detection matters**: Auto-detect and respond in customer's language
- **Test mode is essential**: Clients need to see it work before going live
- **Handoff rules**: Define clear escalation triggers to avoid AI overreach
- **Response templates**: Pre-approved templates for sensitive topics (refunds, complaints)
## Related Links
- [WhatsApp Business API](https://developers.facebook.com/docs/whatsapp)
- [Instagram Messaging API](https://developers.facebook.com/docs/instagram-api/guides/messaging)
- [Google Business Profile API](https://developers.google.com/my-business)