Skip to main content

Multi-agent planning and workflow orchestration system

Project description

Agentic Workflow OS

Structured Architectural Scaffolding for AI Development

Version License

Agentic Workflow OS is a development platform that orchestrates Multi-Agent Systems to plan, architect, and implement complex software projects. Unlike "Chat with Code" tools that rely on messy, unstructured conversation history, this system enforces a Context-First philosophy. It treats Agent Context as a file-system state machine, ensuring that your AI Engineer knows exactly what your AI Architect decided.


⚡ Why Use This?

Most AI coding tools suffer from Context Drift. After 20 messages, the AI forgets the architectural constraints you set in message #1.

AI for projects and work is like a rocket: it gives a quick boost of velocity if you have direction, but soon everyone realizes it's only a first-stage booster that stops midway once context is off. The velocity drops, and to carry forward and build production-grade applications needs a 2nd and 3rd stage booster with precision guidance to reach and stay in orbit. This project is that 2nd and 3rd stage.

Agentic Workflow OS solves this by:

  1. Strict Role Separation: Agents have clearly defined roles with explicit boundaries - architects handle specifications, engineers handle implementation, with enforced separation to prevent scope creep.
  2. Artifact-Driven Handoffs: Agents cannot proceed until they receive specific, validated artifacts from predecessors, ensuring dependencies are met before progression.
  3. Programmatic Agent Interaction: Agents operate in environments with terminal access, using CLI tools to search project state, log decisions, record handoffs, and progress through workflow stages - enabling structured, auditable AI collaboration without conversational drift.

The system operates on a Stateless Core / Stateful Edge model.


🚀 Quick Start

Environment Setup

Create and activate a Python virtual environment:

# Create virtual environment
python3 -m venv myproject-env

# Activate the environment
source myproject-env/bin/activate  

# On Windows: 
source myproject-env\Scripts\activate

Installation

Option A: Pip (Recommended)

pip install agentic-workstation

# First run will launch an interactive setup wizard
agentic --help

First-Time Setup: On first run, Agentic Workflow OS will launch an interactive setup wizard to configure your workspace and preferences. This creates ~/.config/agentic/config.yaml with your settings.

Option B: From Source (Development)

git clone https://github.com/sujith-eag/agentic_workflow.git
cd agentic_workflow
pip install -e .

Uninstallation

Option A: Pip

pip uninstall agentic-workstation

Option B: Docker

# Remove the Docker image
docker rmi agentic-workstation

# Remove any running containers (if any)
docker rm $(docker ps -aq --filter ancestor=agentic-workstation)

CLI & TUI Usage

For detailed CLI documentation, see CLI_REFERENCE.md. For interactive usage, use the Text User Interface (TUI) with agentic.

Available Commands

The CLI uses a context-aware design - commands available depend on whether you're in a project directory or not.

Global Commands (from repository root)

  • agentic init <name> --workflow <type> --description "desc" - Initialize new project with workflow
  • agentic list - List all projects or show details for one
  • agentic delete <name> - Permanently delete a project
  • agentic workflows - List available workflow definitions
  • agentic config - View or edit global configuration
  • agentic - Launch TUI in global mode

Project Commands (from within project directory)

  • agentic status - Show project status and workflow state
  • agentic activate <agent_id> - Activate specific agent session
  • agentic handoff --to <agent_id> --artifacts "files" - Record agent handoff with artifacts
  • agentic decision --title "Title" --rationale "Reason" - Record project decision
  • agentic end - End current workflow session
  • agentic feedback --target <agent_id> --severity <level> --summary "Summary" - Record feedback for an agent or artifact
  • agentic blocker --title "Title" --description "Desc" --blocked-agents "ids" - Record a blocker that prevents progress
  • agentic iteration --trigger "Trigger" --impacted-agents "ids" --description "Desc" - Record an iteration in the development process
  • agentic assumption --assumption "Assumption" --rationale "Rationale" - Record an assumption that may affect the project
  • agentic list-pending - List pending handoffs for the current project
  • agentic list-blockers - List active blockers for the current project

🏗️ Creating & Running a Project

1. Initialize a Project

Navigate to your empty folder and initialize a project.

# From repository root (global context)
agentic init MySaaS --workflow planning --description "My SaaS application project"

Available workflows: planning, implementation, research, workflow-creation

2. Agent Workflow Orchestration

Agents operate in environments with terminal access, using CLI commands to orchestrate structured workflows:

  1. Navigate to Project: cd projects/MySaaS

  2. Session Activation: User or Agents run agentic activate A-01 to start their session and receive context.

  3. Context Processing: Agents access the generated active_session.md file containing their role, instructions, and project state.

  4. Artifact Generation: Agents produce required deliverables and save them to specified artifact paths.

  5. Handoff Execution: Agents execute handoff commands to progress the workflow:

    agentic handoff --to A-02 --artifacts "artifacts/project_brief.md,artifacts/requirements.md"
    

    The system validates artifact existence and workflow rules before allowing progression. The from agent is automatically inferred from the active session.

  6. State Management: Agents can log decisions, record blockers, and query project status using CLI commands throughout the process.

3. Review Status

View the decision tree and current state.

agentic status

📦 Workflows

The system supports pluggable Workflow Manifests with four main workflow types.

1. Planning (15 agents: A-00 to A-14)

  • Goal: Turn a 1-sentence idea into a full Tech Spec.
  • Key Agents:
    • A-00 Orchestrator & Project Controller
    • A-01 Project Guide & Idea Incubation
    • A-02 Planning & Requirements Analyst
    • A-03 Architecture & Technical Design Analyst
    • A-04 Security & Compliance Scrutineer
    • A-05 Infrastructure & DevOps Planning Architect
    • A-06 Data Architecture & Storage Planning Specialist
    • A-07 API & Integration Planning Specialist
    • A-08 UX & Interaction Planning Specialist
    • A-09 Developer Workflow & Engineering Standards Planner

2. Implementation (10 agents: I-00 to I-05 + specialists)

  • Goal: Turn specs into production code.
  • Key Agents:
    • I-00 Implementation Orchestrator
    • I-01 Scaffold & DevOps Architect
    • I-02 Backend & Data Engineer
    • I-03 Frontend & UX Engineer
    • I-04 Quality & Validation Specialist
    • I-05 Release & Integration Manager
    • I-DOC Documentation Sprint Agent
    • I-DS Data Store Implementation Specialist
    • I-PERF Performance Optimization Agent
    • I-SEC Security Hardening Auditor

3. Research (12 agents)

  • Goal: Conduct thorough research, analysis, and reporting.
  • Use Cases: Literature review, data analysis, academic research, market research.

4. Workflow-Creation (9 agents)

  • Goal: Create and customize new workflow packages.
  • Use Cases: Meta-workflow development, process automation.

Custom Workflows

You can create your own workflows by placing a manifest in:

  • Project: ./.agentic/workflows/my-custom-flow/workflow.json
  • User global: ~/.config/agentic/workflows/my-custom-flow/workflow.json
  • System: Bundled in package (not recommended for custom)

Workflow manifests include workflow.json, agents.json, artifacts.json, etc.


🛠️ CLI Commands

Global Commands (from repository root)

# List available workflows
agentic workflows

# Initialize new project
agentic init MyProject --workflow planning --description "My project description"

# Launch TUI (Terminal User Interface)
agentic

# Show CLI help
agentic --help

Project Commands (from within project directory)

# Navigate to project
cd projects/MyProject

# Show current project status
agentic status

# Activate an agent session
agentic activate A-01

# Record handoff between agents (from agent inferred from active session)
agentic handoff --to A-02 --artifacts "requirements.md,architecture.md"

# Record project decision
agentic decision --title "Technology Stack Selection" --rationale "React + Node.js chosen for full-stack consistency"

# End workflow session
agentic end

⚙️ Configuration & Setup

Automatic Setup

On first run, Agentic Workflow OS automatically launches an interactive setup wizard that configures:

  • Default workspace (where projects are stored)
  • Editor command (for opening files)
  • UI preferences and logging levels

Configuration Files

Global Config (~/.config/agentic/config.yaml):

default_workspace: "~/AgenticProjects"
editor_command: "code"
tui_enabled: true
check_updates: true
log_level: "INFO"

Project Config (.agentic/config.yaml in each project):

workflow: planning
strict_mode: true
excluded_paths:
  - "node_modules"
  - ".git"
custom_overrides:
  governance:
    require_reviews: true

Manual Configuration

If you prefer to set up configuration manually or modify existing settings:

  1. Create global config directory:

    mkdir -p ~/.config/agentic
    
  2. Create config file:

    cat > ~/.config/agentic/config.yaml << 'EOF'
    default_workspace: "~/AgenticProjects"
    editor_command: "code"
    tui_enabled: true
    check_updates: true
    log_level: "INFO"
    EOF
    
  3. Create workspace directory:

    mkdir -p ~/AgenticProjects
    

Re-running Setup

To re-run the setup wizard or modify configuration:

# Remove config to trigger setup again
rm ~/.config/agentic/config.yaml
agentic

🛠️ Development

This project is built with Python 3.10+.

  • Core: src/agentic_workflow
  • Manifests: src/agentic_workflow/manifests (Canonical Definitions)
  • Templates: src/agentic_workflow/templates (Jinja2 templates)
  • Build: pyproject.toml (Hatchling)
  • Tests: Not included in deployment

License: MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

agentic_workstation-1.0.9.tar.gz (276.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

agentic_workstation-1.0.9-py3-none-any.whl (384.1 kB view details)

Uploaded Python 3

File details

Details for the file agentic_workstation-1.0.9.tar.gz.

File metadata

  • Download URL: agentic_workstation-1.0.9.tar.gz
  • Upload date:
  • Size: 276.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for agentic_workstation-1.0.9.tar.gz
Algorithm Hash digest
SHA256 666a4b94d80857133ee88b9b3a83edc0dec32aeb89e44d9992f44e34ee2ffbd4
MD5 3246ad1eace22cc7bb9aa8a02724b132
BLAKE2b-256 9c82ae975a0b5064d42a1d01e77f805e202ba2f79e704f81e89d0eaae8c2f7dc

See more details on using hashes here.

File details

Details for the file agentic_workstation-1.0.9-py3-none-any.whl.

File metadata

File hashes

Hashes for agentic_workstation-1.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 a19724e632dfe6c33f8f553752e3344e61cdc6d3c252b01583e9d2435081aa01
MD5 0c016cc3fb737eba57d0505f6b4a9682
BLAKE2b-256 1bfbe65bf22be7cbf3b6a50c5f931932a43612f76de9d9f0a040cbc1c2a72f65

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page