AIWG: Reusable Context & Workflows for AI-Augmented Development

Introduction

AIWG is an innovative multi-agent AI framework designed to augment software development and various other complex tasks. It provides reusable project context and specialist workflows for the AI tools you already use, ensuring consistency and efficiency across sessions. With specialized agents, structured workflows, and multi-platform deployment, AIWG helps teams plan software, coordinate reviews, manage incidents, and maintain operational knowledge. It integrates with popular AI providers like Claude Code, Codex, Copilot, and Cursor, offering a unified approach to AI-augmented work.

Why Use AIWG and Key Benefits

AIWG addresses common challenges in AI-assisted projects, such as maintaining context, recovering from failures, and making quality criteria explicit.

Problems AIWG Solves:

Maintaining Context Across Sessions: Useful decisions often get scattered. AIWG workflows save project outputs in the .aiwg/ directory, including requirements, architecture decisions, risk notes, and test strategies, making them retrievable for later tasks.
Recovering from Failed Attempts: Instead of just retrying, AIWG's agent loops support an execute-and-verify cycle. This records failure information, adapts the next attempt, and stops at configured limits, making the recovery process reviewable.
Making Quality Criteria Explicit: AIWG provides specialist roles and workflows that separate concerns like security, performance, and testing. These roles combine findings and record open decisions, ensuring required artifacts and reviews are ready before work advances.

Six Core Components:

AIWG is built on simple, yet powerful, building blocks that compound in usefulness:

Memory, Structured Semantic Memory: The .aiwg/ directory acts as a persistent artifact repository for project knowledge, grounding later work in recorded decisions.
Reasoning, Multi-Agent Deliberation with Synthesis: Specialist role definitions, like Security Auditor or Test Architect, allow complex artifacts to be routed through required reviewers, with synthesis to merge feedback.
Learning, Closed-Loop Self-Correction (Agent Loop): Executes tasks iteratively, using verification results to guide subsequent attempts and preserving failure analysis.
Verification, Bidirectional Traceability: Supports links between documentation and code, allowing reviewers to inspect relationships and find drift, for example, @implements @.aiwg/requirements/UC-001-login.md.
Planning, Phase Gates with Cognitive Load Management: Structures work using a Stage-Gate methodology, breaking projects into bounded phases with explicit quality criteria and human approval.
Style, Controllable Voice Generation: Voice profiles describe writing preferences, such as formality or technical depth, providing reusable style specifications for content generation.

AIWG also offers a rich catalog of frameworks, including SDLC Complete, Forensics Complete, Media/Marketing Kit, and Research Complete, along with numerous addons like Agent Loop, RLM (Recursive Context Decomposition), and Compound Memory, extending its capabilities across various domains.

Installation

AIWG offers both a prompt-led installer and manual installation via npm.

Prerequisites:

Ensure you have Node.js >=20.0.0 and an AI platform (e.g., Claude Code, GitHub Copilot, Cursor, Warp Terminal).

Prompt-Led Installation (Recommended):

Paste the following into a supported AI provider:

Install or repair AIWG for this project by following
https://aiwg.io/setup.aiwg.yaml
Explain the plan before changing anything, preserve my existing work, and ask
me only for choices you cannot safely determine.

Manual Installation:

For a manual setup, use npm:

npm i -g aiwg
cd /path/to/your/project
aiwg use all --provider claude   # Replace 'claude' with your AI provider selector

After installation, you can check the deployment health and readiness with:

aiwg doctor

You can also deploy specific frameworks instead of all, for example:

aiwg use sdlc --provider claude          # Software development lifecycle
aiwg use forensics --provider claude     # Investigation workflows

Examples

Here are a few ways to leverage AIWG in your projects:

Get a First Useful Result:

After setup, ask your agent to review documentation:

Use AIWG to review this project's README for unclear positioning and missing
onboarding steps. Save a report at
.aiwg/marketing/brand/audit/readme-review.md with file references and the
three highest-priority fixes. Leave the README unchanged.

SDLC Workflow from Idea to Implementation:

Initiate a comprehensive development lifecycle:

Turn an AI-powered code review tool into requirements, architecture decisions, a
first implementation task, and acceptance checks. Return links to the generated artifacts.

Long-Running Implementation Loop:

Fix issues iteratively with defined boundaries:

Fix the failing authentication tests in a bounded loop. Stop after five iterations or forty-five minutes, and
report whether the relevant tests pass and what remains unresolved.

AIWG: Reusable Context & Workflows for AI-Augmented Development

Summary

Repository Information

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