personal-ai-employee
This agent acts like a helpful assistant that automatically manages incoming tasks and projects. It takes work items, breaks them down into clear plans, and guides them through a process for completion, always with a human checking in along the way. This system is useful for anyone who feels overwhelmed by managing multiple projects or tasks, ensuring nothing falls through the cracks. It’s particularly valuable because it works entirely on your computer, keeping your data private and secure, and providing a clear, trackable record of every step. The agent’s strength lies in its organized approach and the ability to automate much of the initial planning and tracking, freeing up your time for the actual work.
README
# Personal AI Employee — Bronze Tier
A local-first autonomous AI workflow system powered by Claude Code.
This project implements the **Bronze Tier Control Plane** of a Personal AI Employee — an AI system that monitors incoming work, generates structured plans, and coordinates tasks through a transparent file-based workflow.
The system is designed around **reliability, transparency, and human-in-the-loop control.**
---
# Architecture Overview
The system follows a simple control-plane architecture:
Watcher → Perception
Orchestrator → Control Loop
Claude CLI → Planning Engine
Human → Approval Authority
```
Incoming Task
│
▼
Drop_Zone / Needs_Action
│
▼
Filesystem Watcher
│
▼
Orchestrator (control loop)
│
▼
Claude CLI
│
▼
Plan Generation
│
▼
Human Approval
│
▼
Execution / Completion
```
---
# Key Features
• Local-first architecture (no cloud dependency)
• File-based workflow engine
• Structured Markdown plans
• Human-in-the-loop approvals
• Deterministic task lifecycle
• Crash-safe orchestrator loop
• Structured JSON logging
• Recoverable workflow state
---
# Project Structure
```
personal-ai-employee/
AI_Employee_Vault/
│
├── Needs_Action/ # Incoming tasks
├── In_Progress/ # Claimed tasks
├── Plans/ # Generated execution plans
├── Pending_Approval/ # Awaiting human approval
├── Approved/
├── Rejected/
├── Done/ # Completed tasks
├── Logs/ # Structured JSON logs
├── Quarantine/ # Malformed files
│
└── Dashboard.md # System activity summary
watcher/
└── filesystem_watcher.py
orchestrator.py
CLAUDE.md # Operational contract
requirements.md
```
---
# Task Lifecycle
Tasks move through the system using **file-based state transitions**.
```
Needs_Action
│
▼
In_Progress
│
▼
Plans
│
▼
Pending_Approval
│
▼
Approved
│
▼
Done
```
Each state transition is expressed through **file movement**, ensuring transparency and auditability.
---
# Claude Operational Contract
The system behavior is defined by:
```
CLAUDE.md
```
This document defines:
• authority boundaries
• workflow lifecycle
• security guardrails
• approval requirements
• error handling rules
It acts as the **operational brain** of the Personal AI Employee.
---
# Orchestrator
The orchestrator provides the **Bronze Tier control loop**.
Responsibilities:
• Start filesystem watcher
• Monitor incoming tasks
• Trigger Claude CLI processing
• Write structured logs
• Retry failed executions
• Recover from transient failures
Execution loop:
```
Every 60 seconds:
1. Scan Needs_Action/
2. If tasks exist
3. Execute Claude CLI
4. Generate plans
5. Log results
```
---
# Watcher
The watcher monitors the filesystem for new tasks and deposits them into:
```
Needs_Action/
```
This acts as the **perception layer** of the system.
---
# Human-in-the-Loop Design
The system never performs sensitive actions automatically.
Actions requiring approval:
• sending emails
• sending messages
• financial transactions
• external API calls
• irreversible operations
Approvals are handled through:
```
Pending_Approval/
```
Users approve actions by moving files to:
```
Approved/
```
---
# Logging
All system activity is written as structured JSON logs:
```
AI_Employee_Vault/Logs/
```
Example log entry:
```
{
"timestamp": "...",
"component": "orchestrator",
"event": "task_detected",
"details": {...},
"result": "success"
}
```
---
# Running the System
Start the orchestrator:
```
python orchestrator.py
```
Optional DRY RUN mode:
```
export DRY_RUN=true
python orchestrator.py
```
---
# Design Principles
This system follows several core principles:
**Local First**
All operations occur within the local vault.
**Transparency**
All state changes occur via file movement.
**Human Authority**
No sensitive action executes without approval.
**Determinism**
Workflow state is always observable.
**Resilience**
Failures do not crash the system.
---
# Roadmap
Future tiers will expand the system:
Bronze
• Local workflow orchestration
Silver
• Multi-source watchers (Gmail, Slack)
• Advanced task parsing
Gold
• Multi-agent coordination
• Autonomous task execution
---
# License
This project is intended for educational and experimental use.
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