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Below is a **complete Product Requirements Document (PRD)** you can directly add to your **GitHub repo (README or `/docs/prd.md`)**.
I wrote it in a **professional engineering style similar to what Stripe / OpenAI / YC startups use**, so it looks strong on your GitHub profile.

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# Autonomous AI Coding Agents (Minions-Style)

### Product Requirements Document (PRD)

---

# 1. Overview

## Product Name

**ForgeAI – Autonomous Software Engineering Agents**

## Vision

ForgeAI is a system of **autonomous AI coding agents** that can take a software development task, understand an existing codebase, generate code changes, validate them through tests, and automatically create a pull request.

The goal is to build a **developer productivity platform** that acts like a **team of AI engineers working alongside humans**.

Inspired by modern internal systems like **AI coding agents used by large engineering organizations**, ForgeAI aims to bring similar capabilities into an open developer platform.

---

# 2. Problem Statement

Modern software development still requires significant manual work for tasks such as:

* implementing small features
* writing boilerplate code
* fixing repetitive bugs
* updating dependencies
* refactoring modules
* writing tests

Even with AI tools like code assistants, developers still:

* manually integrate generated code
* manually run tests
* manually create pull requests
* manually ensure compatibility with existing systems

This creates a gap between **AI code generation** and **actual production engineering workflows**.

ForgeAI bridges this gap by enabling **autonomous AI agents that execute development tasks end-to-end.**

---

# 3. Goals

## Primary Goals

Build a platform that can:

1. Accept software development tasks
2. Analyze an existing codebase
3. Generate production-ready code
4. Run automated validation checks
5. Create pull requests automatically
6. Enable human review and merge

---

## Secondary Goals

* Enable **multi-agent collaboration**
* Support **sandboxed execution environments**
* Maintain **deterministic validation via CI**
* Provide **developer-friendly UI**

---

# 4. Non-Goals

The system will NOT initially aim to:

* fully replace human engineers
* manage production deployments
* perform long-term product planning
* autonomously modify critical infrastructure

ForgeAI will remain a **developer assistant system with human oversight.**

---

# 5. Target Users

### 1. Software Engineers

Engineers who want to automate repetitive coding tasks.

### 2. Startups

Small teams that need higher engineering velocity.

### 3. Open Source Maintainers

Maintainers who want help with routine issues and improvements.

---

# 6. Key Features

## 6.1 Task-Based Development

Users provide a development task:

Example:

```
Add JWT authentication to the API
```

ForgeAI converts the task into actionable engineering steps.

---

## 6.2 Codebase Understanding

Agents must analyze the repository to understand:

* project structure
* frameworks used
* dependency graph
* API patterns
* database schemas

This is done using:

* AST parsing
* embedding-based search
* repository indexing

---

## 6.3 Multi-Agent Architecture

ForgeAI uses multiple specialized agents:

### Product Manager Agent

Breaks user prompts into structured development tasks.

### Architect Agent

Designs the technical approach.

### Backend Agent

Implements server logic.

### Frontend Agent

Creates UI components.

### Database Agent

Handles schema changes and migrations.

### DevOps Agent

Handles testing and CI validation.

---

## 6.4 Sandbox Execution Environment

Each task runs inside an isolated environment.

Capabilities include:

* cloning repositories
* installing dependencies
* executing scripts
* running tests

This prevents the agent from affecting the host system.

---

## 6.5 Automated Code Generation

Agents can:

* create new files
* modify existing files
* refactor modules
* add tests
* update configurations

---

## 6.6 CI Validation

Before creating a pull request, the system runs validation checks:

Examples:

* linting
* type checking
* unit tests
* integration tests

If validation fails, agents attempt automated fixes.

---

## 6.7 Pull Request Automation

When the system determines that changes are valid:

1. create a new git branch
2. commit code changes
3. push branch
4. generate pull request

PR includes:

* description
* summary of changes
* validation results

---

## 6.8 Human-in-the-Loop Review

Developers remain in control.

They can:

* approve PR
* request changes
* reject modifications

---

# 7. User Experience

## Step 1 – Submit Task

User enters a prompt:

```
Add user authentication with JWT
```

---

## Step 2 – Agent Planning

System generates a development plan.

Example:

```
1. Add authentication middleware
2. Create login endpoint
3. Implement token generation
4. Add tests
```

---

## Step 3 – Execution

Agents perform:

* code generation
* testing
* validation

---

## Step 4 – Pull Request Creation

System automatically creates:

```
PR: Add JWT Authentication
```

---

## Step 5 – Human Review

Developer reviews and merges.

---

# 8. System Architecture

## High-Level Architecture

```
User Interface
      │
      ▼
Task Orchestrator
      │
      ▼
Agent Coordinator
      │
 ┌───────────────┐
 │ Agent Workers │
 └───────────────┘
      │
      ▼
Tool Layer
      │
      ▼
Sandbox Environment
      │
      ▼
Validation Layer
      │
      ▼
GitHub Integration
```

---

# 9. Technology Stack

## Frontend

* Next.js
* React
* TailwindCSS

---

## Backend

* Node.js
* TypeScript
* Express / Fastify

---

## Agent Framework

* LangGraph
  or
* OpenAI Agents SDK

---

## LLM Provider

Possible integrations:

* OpenAI
* Anthropic
* local LLMs

---

## Execution Environment

Sandbox options:

* Docker
* Firecracker microVMs

---

## Repository Integration

GitHub APIs for:

* repository cloning
* branch creation
* commits
* pull requests

---

## Database

* PostgreSQL

Used for:

* task tracking
* agent state
* logs

---

# 10. Data Model

## Task

```
Task
 ├ id
 ├ prompt
 ├ repository
 ├ status
 ├ created_at
```

---

## Agent Execution

```
AgentExecution
 ├ agent_type
 ├ task_id
 ├ result
 ├ logs
```

---

## Pull Request

```
PullRequest
 ├ task_id
 ├ branch
 ├ url
 ├ status
```

---

# 11. Security Considerations

Security is critical for autonomous coding systems.

Measures include:

### Sandbox Isolation

All code execution happens in isolated environments.

### Restricted Tool Access

Agents can only access approved tools.

### Secret Protection

Secrets are never exposed to agents.

### Permission Boundaries

Agents cannot push directly to protected branches.

---

# 12. Performance Requirements

System must support:

* parallel agent execution
* large repositories
* fast task planning

Target metrics:

| Metric          | Target   |
| --------------- | -------- |
| Task planning   | < 5 sec  |
| Code generation | < 60 sec |
| PR creation     | < 2 min  |

---

# 13. Observability

System must log:

* agent reasoning steps
* file modifications
* test results
* validation failures

Monitoring tools:

* OpenTelemetry
* structured logging

---

# 14. Future Enhancements

### Long-Running Agents

Agents capable of multi-step workflows.

### Autonomous Bug Fixing

Agents monitor issues and create fixes automatically.

### Dependency Updates

Automated package upgrades.

### Documentation Generation

Agents maintain project documentation.

### Self-Improving Agents

Agents learn from past pull requests.

---

# 15. MVP Scope

Initial version will support:

* single repository
* basic multi-agent planning
* sandbox execution
* automated PR creation

---

# 16. Success Metrics

Product success will be measured by:

* tasks completed autonomously
* PR acceptance rate
* developer adoption
* reduction in manual coding tasks

---

# 17. Risks

### Hallucinated Code

LLMs may produce incorrect implementations.

Mitigation:

* CI validation
* human review

---

### Security Risks

Agents interacting with repositories could introduce vulnerabilities.

Mitigation:

* sandboxing
* permission controls

---

# 18. Conclusion

ForgeAI represents the next evolution of developer tooling:

**AI agents that can participate directly in the software development lifecycle.**

By combining:

* LLM reasoning
* tool integration
* sandboxed execution
* CI validation

ForgeAI enables a **collaborative future where humans and AI engineers build software together.**

---

If you want, I can also help you create **three extremely powerful GitHub additions** that will make this project look **10× more impressive to recruiters**:

1. **System architecture diagram (like Stripe / Uber engineering blogs)**
2. **Full repo folder structure**
3. **Implementation roadmap (so contributors know what to build)**

These three things make a project look **senior-engineer level instead of side-project level.**