Staff MVP - Product Requirements Document

Version: 1.0
Date: July 27, 2025
Document Owner: Product Manager

Product overview

Product summary

Staff MVP is a lightweight AI multi-agent development system designed to automate software development tasks through specialized AI agents. The system enables a single user to create, assign, and manage development tasks while AI agents autonomously process these tasks and submit solutions via GitHub pull requests. Built as a CLI application on existing Go infrastructure, this MVP focuses on core functionality that can be delivered within one week.

The project leverages existing components including LLM integration, agent framework, task management interfaces, and Git operations to create a functional multi-agent system that bridges task management with automated code generation and submission.

Goals

Business goals

Deliver a working multi-agent system within one week development timeline
Validate the core concept of AI agents autonomously handling development tasks
Create foundation for future enterprise multi-agent development platforms
Demonstrate integration capabilities with existing development workflows
Establish proof of concept for automated task-to-PR workflow

User goals

Streamline development task management through AI automation
Reduce manual effort in routine development tasks
Maintain oversight and control over AI-generated solutions through PR review process
Integrate seamlessly with existing Git-based development workflows
Enable parallel task processing through multiple specialized agents

Non-goals

Multi-user authentication and authorization systems
PR comment handling and automated responses
Self-learning or AI model improvement capabilities
Complex task dependency management
Real-time collaboration features
Web-based user interface
Enterprise user management

User personas

Primary user: Solo developer/Technical lead

Profile: Experienced software developer or technical lead working on projects requiring task automation and code generation assistance.

Needs:

Efficient task creation and assignment workflow
Automated code generation for routine tasks
Maintained control over code quality through PR reviews
Integration with existing Git workflows
Visibility into agent processing status

Technical proficiency: High - comfortable with CLI tools, Git operations, and development workflows

Goals: Increase development velocity while maintaining code quality and project oversight

Role-based access

Since this is a single-user system, all functionality is available to the primary user without access restrictions. Future versions may implement role-based permissions for team collaboration.

Functional requirements

High priority

FR-001: Task management system

Create tasks via CLI commands with title, description, and metadata
Store tasks as markdown files in Git repository
Support task states: todo, in_progress, completed, archived
Enable task priority assignment: low, medium, high
Allow manual task assignment to specific agents

FR-002: Multi-agent system

Support minimum 4 agent roles: CEO, Product Manager, Backend Engineer, Frontend Engineer
Load agent configurations from YAML/JSON files
Enable agents to run in continuous processing loops
Allow agents to claim and process assigned tasks
Support agent-specific LLM provider configurations

FR-003: LLM integration

Support multiple LLM providers: OpenAI, xAI, Claude, Gemini
Enable provider-specific configuration and API key management
Implement unified interface for agent-LLM communication
Handle LLM request failures and retries

FR-004: GitHub integration

Create feature branches for task solutions
Generate pull requests with task metadata and solutions
Support automated commit message formatting
Enable PR template customization

FR-005: CLI interface

Implement core commands: create-task, assign-task, start-agent, list-tasks
Provide task status monitoring and agent activity logging
Support configuration management through CLI
Enable agent start/stop controls

Medium priority

FR-006: Task dependencies

Allow tasks to reference other tasks
Support basic dependency checking before task processing
Enable agents to create follow-up tasks

FR-007: Error handling

Implement graceful handling of LLM API failures
Provide task retry mechanisms for failed operations
Log agent errors and processing issues
Support manual task reassignment after failures

FR-008: Configuration management

Store agent configurations in version-controlled files
Support environment-based configuration overrides
Enable runtime configuration updates without restarts

Low priority

FR-009: Reporting and metrics

Track task completion rates and processing times
Generate basic agent performance reports
Monitor LLM API usage and costs

FR-010: Advanced task features

Support task templates for common operations
Enable bulk task creation from files
Implement task search and filtering

User experience

Entry points

Primary entry point: Command-line interface with intuitive command structure

staff create-task --title "Feature X" --description "..." --priority high
staff assign-task <task-id> --agent backend-engineer
staff start-agent --name backend-engineer --config config.yaml

Core experience

Task creation workflow
- User creates task via CLI with title, description, and optional metadata
- System generates unique task ID and stores as markdown file in repository
- Task appears in todo state, available for assignment
Agent processing loop
- Agent polls for assigned tasks in todo state
- Agent updates task to in_progress and processes with configured LLM
- Agent creates feature branch, commits solution, and submits PR
- Task status updates to completed upon successful PR creation
Monitoring and oversight
- User monitors agent activity through CLI status commands
- User reviews and merges PRs containing agent solutions
- System maintains audit trail of all agent actions

Advanced features

Multi-agent orchestration: Multiple agents running simultaneously processing different tasks
Task chaining: Agents creating follow-up tasks based on solution requirements
Configuration hot-reloading: Updating agent configurations without system restart

UI/UX highlights

Command discoverability: Help system and command autocompletion
Progress visibility: Real-time status updates during agent processing
Error transparency: Clear error messages with suggested remediation steps
Configuration simplicity: Minimal setup required to start processing tasks

Narrative

As a developer managing multiple projects, I open my terminal and create a new development task using the Staff CLI. The system immediately stores the task in my Git repository and assigns it a unique identifier. I assign the task to my backend engineer agent, which I've configured with specific expertise and LLM preferences. The agent autonomously picks up the task, analyzes the requirements using its configured LLM, generates a solution, creates a feature branch, and submits a pull request for my review. Meanwhile, I can create additional tasks and assign them to other specialized agents, enabling parallel processing of multiple development initiatives. Each completed task results in a clean PR that I can review, modify if needed, and merge into my main branch, maintaining full control over code quality while benefiting from AI-powered automation.

Success metrics

User-centric metrics

Task completion rate: Percentage of created tasks successfully processed to PR stage
Time to PR: Average time from task assignment to PR creation
User adoption of CLI commands: Frequency of core command usage
Task creation velocity: Number of tasks created per day/week

Business metrics

Development velocity improvement: Reduction in time for routine development tasks
Code quality maintenance: PR merge rate and review feedback quality
System reliability: Uptime and successful task processing percentage
Feature utilization: Usage distribution across different agent types and task categories

Technical metrics

Agent processing efficiency: Average task processing time per agent type
LLM API performance: Response times and success rates across providers
System resource utilization: CPU and memory usage during multi-agent operations
Error rates: Frequency and types of task processing failures

Technical considerations

Integration points

Git repository integration: Seamless read/write operations for task storage and branch management
GitHub API integration: Automated PR creation and repository management
LLM provider APIs: Reliable communication with multiple AI service providers
Local filesystem: Configuration file management and logging systems

Data storage and privacy

Task data: Stored as markdown files in Git repository, version-controlled and backed up
Configuration data: YAML/JSON files for agent configurations and system settings
API keys: Environment variable management with no hardcoded credentials
Privacy considerations: All data remains local or in user-controlled repositories

Scalability and performance

Concurrent agent processing: Support for multiple agents running simultaneously
LLM rate limiting: Respect provider API limits and implement backoff strategies
Git operation optimization: Efficient branch creation and PR submission
Memory management: Stateless agent design for minimal resource consumption

Potential challenges

LLM API reliability: Handle service outages and rate limiting gracefully
Git conflict resolution: Manage potential merge conflicts from concurrent agent operations
Task complexity boundaries: Define clear limits for agent capabilities
Configuration complexity: Balance flexibility with ease of setup

Milestones and sequencing

Project estimate

Total timeline: 5-7 days for core MVP functionality

Team size: 1-2 developers (primary developer plus optional reviewer)

Suggested phases

Phase 1: Foundation (Days 1-2)

Enhance existing CLI framework with core task management commands
Implement task storage system using existing Git operations
Create basic agent configuration loading

Phase 2: Agent processing (Days 3-4)

Integrate agent processing loop with existing LLM providers
Implement task assignment and status management
Build GitHub PR creation functionality

Phase 3: Integration and polish (Days 5-6)

End-to-end testing of task-to-PR workflow
Error handling and logging improvements
Documentation and usage examples

Phase 4: Validation (Day 7)

User acceptance testing with real development tasks
Performance optimization and bug fixes
Deployment preparation and final documentation

User stories

Core functionality

US-001: Create development task Description: As a developer, I want to create a new development task via CLI so that I can track work that needs to be completed. Acceptance criteria:

CLI command accepts title, description, and optional priority
Task is assigned unique identifier and stored as markdown file in repository
Task appears in todo state and is available for assignment
Command provides immediate feedback with task ID and file location

US-002: Assign task to agent Description: As a developer, I want to assign a specific task to a named agent so that the appropriate AI agent can process it. Acceptance criteria:

CLI command accepts task ID and agent name
System validates agent exists and is configured
Task metadata updates to reflect assignment
Agent can discover assigned task in next polling cycle

US-003: Start agent processing Description: As a developer, I want to start an agent in processing mode so that it can autonomously handle assigned tasks. Acceptance criteria:

CLI command starts agent with specified configuration
Agent enters continuous polling loop for assigned tasks
Agent status is visible through monitoring commands
Agent can be stopped gracefully via CLI or signal

US-004: Agent processes task Description: As an AI agent, I want to process an assigned task and create a solution so that development work can be automated. Acceptance criteria:

Agent retrieves task details from repository
Agent sends task to configured LLM for solution generation
Agent creates feature branch with naming convention task/{task-id}-{clean-title}
Agent commits solution with formatted commit message
Agent creates PR with task metadata and solution description

US-005: Monitor agent activity Description: As a developer, I want to monitor agent processing status so that I can track progress and identify issues. Acceptance criteria:

CLI command shows active agents and their current tasks
Real-time status updates during task processing
Error messages and logs are accessible via CLI
Processing history is maintained for completed tasks

Task management

US-006: List tasks by status Description: As a developer, I want to view tasks filtered by status so that I can understand current workload and priorities. Acceptance criteria:

CLI command lists tasks with status filtering options
Output includes task ID, title, status, assignee, and creation date
Support for status filters: todo, in_progress, completed, archived
Pagination support for large task lists

US-007: Update task priority Description: As a developer, I want to modify task priority so that agents can process high-priority items first. Acceptance criteria:

CLI command accepts task ID and new priority level
Valid priority levels: low, medium, high
Task metadata updates immediately
Agents respect priority ordering when selecting tasks

US-008: Agent creates follow-up task Description: As an AI agent, I want to create additional tasks based on my solution so that complex work can be broken down appropriately. Acceptance criteria:

Agent can create new tasks during processing
New tasks reference parent task ID
Created tasks appear in todo state for assignment
Parent task includes references to created subtasks

Configuration and setup

US-009: Configure agent with LLM provider Description: As a developer, I want to configure an agent with specific LLM provider settings so that it can generate appropriate solutions. Acceptance criteria:

Configuration file specifies agent name, role, LLM provider, and credentials
Support for OpenAI, xAI, Claude, and Gemini providers
Agent validates configuration before starting processing
Configuration changes take effect on agent restart

US-010: Configure agent role and expertise Description: As a developer, I want to define agent roles and expertise areas so that tasks are processed by appropriate specialists. Acceptance criteria:

Agent configuration includes role definition and system prompt
Pre-defined roles: CEO, Product Manager, Backend Engineer, Frontend Engineer
Custom system prompts can be loaded from markdown files
Role definitions affect task assignment recommendations

Error handling

US-011: Handle LLM API failures Description: As an agent, I want to gracefully handle LLM API failures so that temporary issues don't permanently block task processing. Acceptance criteria:

Agent retries failed API calls with exponential backoff
Maximum retry attempts configurable per provider
Failed tasks return to todo state after max retries exceeded
Error details logged for debugging and user visibility

US-012: Recover from Git operation failures Description: As an agent, I want to handle Git operation failures so that repository issues don't prevent task completion. Acceptance criteria:

Agent handles branch creation conflicts by using alternative names
Failed PR creation attempts are retried with updated metadata
Git authentication errors provide clear user guidance
Repository state inconsistencies are detected and reported

US-013: Validate task assignment Description: As a developer, I want the system to validate task assignments so that only properly configured agents receive work. Acceptance criteria:

System checks agent exists and is configured before assignment
Agent capability matching suggests appropriate agents for task types
Invalid assignments are rejected with helpful error messages
Unassigned tasks remain available for later assignment

GitHub integration

US-014: Create formatted pull request Description: As an agent, I want to create well-formatted pull requests so that solutions are clearly documented for review. Acceptance criteria:

PR title includes task ID and descriptive summary
PR description includes task details, solution approach, and testing notes
Commit messages follow conventional format with task reference
PR is linked to original task file for traceability

US-015: Handle PR creation conflicts Description: As an agent, I want to handle PR creation conflicts so that multiple agents can work simultaneously. Acceptance criteria:

System detects when branch names already exist
Alternative branch naming strategies prevent conflicts
PR creation failures are logged and tasks returned to assignable state
User receives notification of conflicts requiring manual intervention

Advanced features

US-016: Support task templates Description: As a developer, I want to create tasks from templates so that common work patterns can be standardized. Acceptance criteria:

CLI command creates task from named template file
Templates include placeholder substitution for dynamic content
Template library is stored in repository for version control
Custom templates can be created and shared

US-017: Bulk task operations Description: As a developer, I want to perform operations on multiple tasks so that I can efficiently manage large workloads. Acceptance criteria:

CLI supports task selection by status, priority, or assignee
Bulk operations include assign, update-priority, and archive
Operations provide progress feedback and error summaries
Changes are atomic where possible to prevent partial failures

US-018: Agent performance metrics Description: As a developer, I want to view agent performance metrics so that I can optimize task assignment and configuration. Acceptance criteria:

CLI command shows completion rates per agent
Average processing times displayed by task type and agent
LLM API usage statistics and cost tracking
Performance trends over configurable time periods