iris

AI-native software development with multi-agent orchestration.

iris implements the AI-Driven Development Lifecycle (AI-DLC) methodology as a set of markdown-based agents that work with your favorite AI coding tools.


---

What is AI-DLC?

AI-DLC is a reimagined software development methodology where AI drives the conversation and humans validate. Unlike traditional Agile where iterations span weeks, AI-DLC operates in Bolts - rapid iterations measured in hours or days.

> "Traditional development methods were built for human-driven, long-running processes. AI-DLC reimagines the development lifecycle with AI as a central collaborator, enabling rapid cycles measured in hours or days rather than weeks."

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| Aspect | Agile/Scrum | AI-DLC |
|--------|-------------|--------|
| Iteration duration | Weeks (Sprints) | Hours/days (Bolts) |
| Who drives | Human-driven, AI assists | AI-driven, human-validated |
| Design techniques | Out of scope | Integrated (DDD, TDD, BDD) |
| Task decomposition | Manual | AI-powered |
| Phases | Repeating sprints | Rapid three-phase cycles (Inception → Construction → Operations) |
| Rituals | Daily standups, retrospectives | Mob Elaboration, Mob Construction |

---

How It Works

iris provides four specialized agents that guide you through the entire development lifecycle:

``
┌─────────────────┐
│ Master Agent │ Orchestrates & navigates
└────────┬────────┘
│
┌────────────────────┼────────────────────┐
▼ ▼ ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ Inception │ → │ Construction │ → │ Operations │
│ Agent │ │ Agent │ │ Agent │
└───────────────┘ └───────────────┘ └───────────────┘
Capture intent Execute bolts Deploy & monitor
Define units Build & test Verify & scale
Plan stories Validate stages
`

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| Phase | Agent | Purpose | Key Outputs |
|-------|-------|---------|-------------|
| Inception | Inception Agent | Capture intents, elaborate requirements, decompose into units | User stories, NFRs, Unit definitions, Bolt plans |
| Construction | Construction Agent | Execute bolts through Domain Design → Logical Design → Code Generation → Testing | Domain designs, Logical designs, Code, Tests |
| Operations | Operations Agent | Deploy, verify, and monitor | Deployment units, Monitoring, Runbooks |

---

Quick Start

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`bash
npx project-iris install
`

The installer detects your AI coding tools (Claude Code, Cursor, GitHub Copilot) and sets up:
- Agent definitions and skills
- Memory bank structure for context persistence
- Slash commands for easy agent invocation

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`bash


Start the Master Agent

/iris-master-agent

Then type:

project-init
`

This guides you through establishing:
- Tech Stack - Languages, frameworks, databases, infrastructure
- Coding Standards - Formatting, linting, naming, testing strategy
- System Architecture - Architecture style, API design, state management
- UX Guide - Design system, styling, accessibility (optional)
- API Conventions - API style, versioning, response formats (optional)

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`bash
/iris-inception-agent intent-create
`

An Intent is your high-level goal:
- "User authentication system"
- "Product catalog with search"
- "Payment processing integration"

The agent will:
1. Ask clarifying questions to minimize ambiguity
2. Elaborate into user stories and NFRs
3. Define system context
4. Decompose into loosely-coupled units

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`bash


Plan bolts for your stories

/iris-inception-agent bolt-plan

Execute a bolt

/iris-construction-agent bolt-start
`

Each bolt goes through validated stages:
1. Domain Design - Model business logic using DDD principles
2. Logical Design - Apply patterns and make architecture decisions
3. ADR Analysis - Document significant decisions (optional)
4. Code Generation - Generate production code
5. Testing - Verify correctness with automated tests

Human validation happens at each stage gate.

---

Project Scenarios

iris supports both new projects and existing codebases.

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Starting fresh with no existing code:

`bash
/iris-master-agent


Then type: project-init


`

1. Initialize project with standards (tech stack, coding standards, architecture)
2. Begin Inception phase immediately
3. Build from scratch using AI-DLC methodology

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Adding features to an existing codebase requires understanding what's already there:

`bash
/iris-master-agent


Then type: code-elevate


`

Code Elevation analyzes your existing codebase and creates:

- Static Model (memory-bank/elevation/static-model.md)
- Components and their responsibilities
- Relationships between components
- Key abstractions and patterns used

- Dynamic Model (memory-bank/elevation/dynamic-model.md)
- How components interact for significant use cases
- Request/response flows
- Event chains and data transformations

After code elevation, agents have full context about your existing architecture, enabling them to:
- Propose changes that fit existing patterns
- Identify integration points for new features
- Avoid breaking existing functionality

---

Key Concepts

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A high-level statement of purpose that encapsulates what needs to be achieved - whether a business goal, feature, or technical outcome. It serves as the starting point for AI-driven decomposition.

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A cohesive, self-contained work element derived from an Intent. Units are loosely coupled and can be developed independently. Analogous to a Subdomain (DDD) or Epic (Scrum).

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The smallest iteration in AI-DLC, designed for rapid implementation. Unlike Sprints (weeks), Bolts are hours to days. Each bolt encapsulates a well-defined scope of work.

| Type | Best For | Stages |
|------|----------|--------|
| DDD Construction | Complex business logic, domain modeling | Domain Design → Logical Design → ADR → Code Generation → Testing |
| Simple Construction | UI, integrations, utilities | Plan → Code Generation → Testing |

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File-based storage for all project artifacts. Maintains context across agent sessions and provides traceability between artifacts.

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Project decisions that inform AI code generation. Standards ensure consistency across all generated code and documentation.

---

Project Structure

After installation:

`
.iris/
├── manifest.yaml # Installation manifest
└── aidlc/ # AI-DLC flow
├── agents/ # Agent definitions
├── skills/ # Agent capabilities
├── templates/ # Artifact templates
│ └── standards/ # Standards facilitation guides
└── memory-bank.yaml # Memory bank schema

memory-bank/ # Created after project-init
├── project.yaml # Project configuration
│
├── elevation/ # Brownfield code analysis (if applicable)
│ ├── static-model.md # Components, responsibilities, relationships
│ └── dynamic-model.md # Use case interactions
│
├── intents/ # Your captured intents
│ └── {NNN}-{intent-name}/
│ ├── requirements.md # Functional & non-functional requirements
│ ├── prfaq.md # Press Release / FAQ
│ ├── risks.md # Risk assessment
│ ├── system-context.md # System boundaries & actors
│ ├── units.md # Unit decomposition overview
│ └── units/
│ └── {UUU}-{unit-name}/
│ ├── unit-brief.md
│ └── stories/
│
├── bolts/ # Bolt execution records
│ └── {BBB}-{unit-name}/
│ ├── bolt.md # Bolt instance metadata
│ ├── ddd-01-domain-design.md
│ ├── ddd-02-logical-design.md
│ ├── adr-{N}-{slug}.md # ADR (optional)
│ └── ddd-03-test-report.md
│
├── standards/ # Project standards
│ ├── tech-stack.md
│ ├── coding-standards.md
│ └── system-architecture.md
│
└── operations/ # Deployment context
├── deployment-units/
└── playbooks/
`

---

Agent Commands

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`bash
/iris-master-agent
`
| Command | Purpose |
|---------|---------|
| project-init | Initialize project with standards |
| code-elevate | Analyze existing codebase (brownfield) |
| analyze-context | View current project state |
| route-request | Get directed to the right agent |
| explain-flow | Learn about AI-DLC methodology |
| answer-question | Get help with any iris question |

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`bash
/iris-inception-agent
`
| Command | Purpose |
|---------|---------|
| intent-create | Create a new intent with PRFAQ |
| intent-list | List all intents |
| requirements | Gather requirements with measurements |
| risks | Assess risks with mitigations |
| context | Define system context |
| units | Decompose into units |
| story-create | Create stories for a unit |
| bolt-plan | Plan bolts for stories |
| review | Review inception artifacts |

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`bash
/iris-construction-agent
`
| Command | Purpose |
|---------|---------|
| bolt-start | Start/continue executing a bolt |
| bolt-status | Check bolt progress |
| bolt-list | List all bolts |
| bolt-replan | Replan bolts if needed |

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`bash
/iris-operations-agent
`
| Command | Purpose |
|---------|---------|
| build | Build deployment artifacts |
| deploy | Deploy to environment |
| verify | Verify deployment |
| monitor | Set up monitoring and SLOs |
| rollback | Rollback to previous version |

---

Why iris?

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Built from the ground up for AI-driven development. AI-DLC is a reimagination based on first principles, not a retrofit of existing methods.

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Validation at each stage catches errors early before they cascade downstream. Each validation transforms artifacts into rich context for subsequent stages.

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DDD, TDD, and BDD are integral to the methodology - not optional add-ons. This addresses the "whitespace" in Agile that has led to quality issues.

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Works with Claude Code, Cursor, GitHub Copilot, and other AI coding assistants. Markdown-based agents work anywhere.

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Specs and Memory Bank provide structured context for AI agents. Agents reload context each session - no more lost knowledge.

---

Supported Tools

| Tool | Status | Installation |
|------|--------|--------------|
| Claude Code | Full support | Slash commands in .claude/commands/ |
| Cursor | Full support | Rules in .cursor/rules/ (.mdc format) |
| GitHub Copilot | Full support | Agents in .github/agents/ (.agent.md format) |
| Google Antigravity | Full support | Agents in .agent/agents/ |

---

FAQ

Q: Agents don't seem to remember previous context?
Each agent invocation starts fresh. Agents read context from the Memory Bank at startup. Ensure artifacts are saved after each step.

Q: How do I reset project state?
Clear the memory-bank/ directory to reset all artifacts. To remove iris entirely, delete the .iris/ directory and tool-specific command files.

Q: Can I use iris with existing Agile workflows?
AI-DLC is designed as a reimagination, not a retrofit. However, familiar concepts (user stories, acceptance criteria) are retained to ease transition.

Q: What project types is this suited for?
iris is designed for building complex systems that demand architectural complexity, trade-off management, and scalability. Simpler systems may be better suited for low-code/no-code approaches.

---

Resources

- AI-DLC Specification (AWS)

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Analytics & Privacy

iris collects anonymous usage analytics during installation only. This helps us understand adoption and improve the product.

What we collect: OS, shell type, selected IDEs, installation success/failure, approximate location (country level).

What we don't collect: No usernames, no file paths, no project contents, no IP addresses stored.

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`bash


Option 1: Environment variable

IRIS_TELEMETRY_DISABLED=1 npx project-iris@latest install

Option 2: DO_NOT_TRACK standard

DO_NOT_TRACK=1 npx project-iris@latest install
``

Analytics are automatically disabled in CI environments.

See PRIVACY.md for full details.

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License

MIT License - see LICENSE for details.