HandoffOS
# HandoffOS
> **Enterprise Workflow Intelligence through MCP**
>
> **Rules detect. AI explains. MCP acts.**




---
## Overview
Large enterprises don't lose productivity because employees cannot communicate—they lose productivity because work gets **stuck during handoffs between teams**.
Manager approval may be complete, but HR assumes IT has already created a laptop request. Identity provisioning cannot begin until the laptop is allocated, VPN access depends on identity, and onboarding stalls because no system understands the complete workflow.
**HandoffOS** solves this problem by reconstructing enterprise events into a **live workflow state**, detecting bottlenecks using deterministic business rules, explaining them with AI-backed natural language, and exposing workflow capabilities through the **Model Context Protocol (MCP)**.
Rather than acting as another enterprise chatbot, HandoffOS functions as a **Workflow Intelligence Engine** that any MCP-compatible AI client can use.
---
# Problem Statement
Modern enterprises operate using multiple disconnected systems such as:
- HR Platforms
- Ticketing Systems
- Email
- Calendar
- Documentation
- Task Boards
Each platform understands only a small portion of an organization's workflow.
This creates problems such as:
- Unknown workflow bottlenecks
- Missed SLAs
- Poor ownership visibility
- Delayed onboarding
- Manual follow-ups
- Lack of operational transparency
Organizations know **tasks**.
They rarely know the **state of the workflow**.
---
# Solution
HandoffOS continuously transforms enterprise events into a single workflow graph.
Instead of showing independent tasks, it understands:
- Workflow Dependencies
- Ownership
- Critical Paths
- Workflow Health
- Business Rules
The system identifies the root blocker, explains why it exists using evidence, predicts downstream impact, and recommends or executes the next approved action.
---
# Key Features
### Live Workflow State
Visualizes the current workflow and immediately highlights blocked stages.
---
### Deterministic Rules Engine
Business logic detects:
- Missing Dependencies
- Missing Owners
- SLA Violations
- Critical Path Blocks
- Missing Documentation
Rules determine facts.
AI never invents them.
---
### Evidence-Based Explanations
Every blocker includes supporting evidence such as:
- Completed approvals
- Missing tasks
- Dependency chains
- Rule violations
This eliminates hallucinations and increases trust.
---
### Workflow Health Score
Calculates workflow health based on measurable factors including:
- Blocked Nodes
- SLA Violations
- Critical Path Risk
- Downstream Impact
The score is explainable and reproducible.
---
### Workflow Simulation
"What happens if this blocker is resolved?"
The workflow is recalculated instantly, showing:
- Updated Health
- New Critical Path
- Estimated Completion Time
---
### Execute Approved Actions
Supports controlled workflow execution such as:
- Creating Tasks
- Assigning Owners
- Scheduling Activities
- Updating Workflow State
- Writing Audit Logs
---
### MCP Native
HandoffOS exposes its capabilities using the **Model Context Protocol**.
Any compatible client—including NitroStudio, Claude Desktop, and future MCP-enabled assistants—can access the same workflow intelligence through a standard interface.
---
# Architecture
```
Enterprise Events
│
▼
Event Store
│
▼
Workflow State Builder
│
▼
Deterministic Rules Engine
│
▼
Evidence Generation
│
▼
MCP Server
├── Resources
├── Tools
└── Prompts
│
▼
NitroStudio • Claude • Cursor • MCP Clients
```
---
# Demonstration Workflow
Our prototype demonstrates **Employee Onboarding**.
```
Manager Approval
│
▼
HR Verification
│
▼
Laptop Allocation
│
▼
Identity Access
│
▼
VPN Setup
│
▼
Developer Access
│
▼
Orientation
```
If **Laptop Allocation** is delayed,
every downstream activity becomes blocked.
HandoffOS detects the root cause immediately.
---
# MCP Resources
```
workflow://onboard-priya/state
workflow://onboard-priya/events
workflow://onboard-priya/findings
workflow://onboard-priya/audit-log
workflow://rules
```
---
# MCP Tools
- detect_blockers
- simulate_resolution
- execute_action
- ingest_event
- plan_next_actions
---
# MCP Prompts
- explain_blocker
- manager_summary
---
# Technology Stack
- TypeScript
- Node.js
- NitroStack
- Model Context Protocol (MCP)
---
# Installation
```bash
git clone https://github.com/AdithyanandanArun/HandOffOS.git
cd HandOffOS
npm install
```
Run the server:
```bash
npm run start
```
---
# Connect Using an MCP Client
Configure your MCP client:
```json
{
"mcpServers": {
"handoffos": {
"url": "YOUR_SERVER_URL"
}
}
}
```
Supported clients include:
- NitroStudio
- Claude Desktop
- Cursor
- Other MCP-compatible clients
---
# Potential Applications
Although demonstrated using employee onboarding, HandoffOS can support:
- Procurement
- Vendor Onboarding
- Customer Support
- Leave Approval
- Incident Response
- Manufacturing Workflows
- IT Service Management
Only the workflow template changes.
The engine remains the same.
---
# Why HandoffOS?
Most AI systems answer questions.
HandoffOS improves workflows.
```
Question
│
▼
Evidence
│
▼
Rules
│
▼
AI Explanation
│
▼
Simulation
│
▼
Approved Action
│
▼
Updated Workflow
```
The result is a transparent, explainable, and actionable workflow intelligence platform.
---
# Team
Developed during the **Agentic AI Hackathon 2026**
---
# License
MIT License
---
> **HandoffOS makes invisible enterprise handoffs visible, explainable, and actionable.**
TDQS
Scored across 13 tools
Most tools are distinct, but what_if_multi and simulate_resolution both simulate without changing live workflow state, differing mainly in scope (multi-node vs. single resolution), which could confuse an agent. Other tools are clearly separated by action and resource.
The naming convention is overwhelmingly verb_noun (ingest_event, detect_blockers, execute_action), but 'what_if_multi' breaks the pattern by not starting with a verb. No camelCase or mixed conventions exist, so the inconsistency is minor.
Thirteen tools is well within the optimal 3-15 range and each tool addresses a distinct aspect of workflow management, from simulation and execution to audit and comparison, so the count feels appropriately scoped.
The set covers core workflow operations like simulation, execution, rollback, prediction, and audit, but lacks an explicit approval tool even though plan_next_actions references approvals. Additionally, workflow retrieval is only implicit through compare_workflows, leaving a moderate gap in the lifecycle.