AI Dev Workflow

Reusable, provider-neutral workflow for AI-assisted software development.

This repository provides a reusable, provider-neutral AI development workflow with first-class brainstorming, spec, ADR, implementation plan, tests, PR evidence and runtime verification artifacts.

Default lifecycle

Brainstorm
  -> Spec
  -> ADR (when needed)
  -> Issue Ready Freeze
  -> Implementation Plan
  -> TesterAgent writes executable tests
  -> RED
  -> ImplementerAgent implements
  -> GREEN + regression
  -> ReviewerAgent (automatic independent review by default)
  -> Integration
  -> Runtime verification batch when required
  -> Promotion / Done

Roles

• ArchitectAgent: brainstorm, spec, ADR and acceptance criteria.
• TesterAgent: derives tests from the frozen contract and proves RED before implementation.
• ImplementerAgent: implements without changing the frozen test contract; must reach GREEN and run applicable regression checks.
• ReviewerAgent: automatic semantic/architecture reviewer by default; must use a different worker_id from the Implementer. Separate credential/identity is optional.
• RuntimeVerifierAgent: optional, project-specific runtime verification role.

A role is not a model. GPT, Codex, Claude, local models or future providers are replaceable adapters under these contracts.

Core rules

• Work item is the task source of truth through a configurable WorkItemProvider (GitHub Issues or Azure DevOps).
• Ready is the commitment/freeze boundary.
• Test design happens before implementation.
• Implementer may report a bad test/spec as blocked, but must not rewrite expectations merely to make tests pass.
• Deterministic CI gates run before automatic independent semantic review.
• Runtime verification is separate from offline/CI verification.
• Projects consume versioned reusable workflows such as @v1; they do not copy the engine.

Consumer project

A project keeps only project-specific context/config:

AGENTS.md
CURRENT.md (optional bootstrap/router)
.ai-workflow.yml
.github/workflows/ai.yml
project tests/runtime rules

Its caller workflow will reference this repository's reusable workflow.

The framework does not install its own CI/check workflow into consumer repositories, does not configure required status checks, and does not modify branch protection. Existing consumer-repository build, test, lint, security, approval, and merge policies remain authoritative. The generated .github/workflows/ai-workflow.yml is orchestration only.

See:

• AGENTS.md
• docs/lifecycle.md
• docs/agent-contracts.md
• docs/tdd-handoff.md
• .ai-workflow.example.yml

Refs #1

Execution environments

Execution environments are provider-neutral. The first supported project contract is GitHub Codespaces with three modes:

• off: never use Codespaces.
• on-demand: provision/start a Codespace only when a routed task requires it.
• always-on: keep a Codespace available according to project policy.

The recommended default is on-demand.

Agent role, provider and environment are independent dimensions. For example, ImplementerAgent may run through different provider adapters while using the same Codespaces workspace.

Project bootstrap

The central framework owns the shared repository metadata contract.

Consumer repositories can run the reusable bootstrap-project.yml workflow to create or update the managed workflow labels. Bootstrap is idempotent and does not delete project-specific labels.

Managed labels currently include:

• workflow states: status:ready, status:in-progress, status:blocked, status:in-review, status:runtime-pending, status:release-pending;
• priorities: priority:P0, priority:P1, priority:P2.

This keeps label semantics portable when the framework or a project is copied to another repository/account.

Workspace planning context

Multi-repository brainstorming uses a workspace registry in .ai-workspace.yml.

Before a work item is published, the Planning Context Resolver normalizes:

• repositories and their purpose/ref/access;
• context paths relevant to architecture/brainstorming;
• the default target repository;
• GitHub Project identity;
• logical initiatives and repository-local milestone mappings.

ArchitectAgent then emits a planning decision identifying the correct target repository, affected repositories, initiative/release, dependencies, issue type, and runtime requirement.

This keeps brainstorming portable across ChatGPT, Claude, Codex, local models, or future providers and prevents repository selection from depending on model memory.

Azure DevOps initialization

Azure DevOps is supported through the provider-neutral WorkItemProvider boundary.

Run:

AZURE_DEVOPS_TOKEN=... ./bin/ai-workflow init-ado \
  --organization <org> \
  --project <project>

The initializer discovers the project's current process, Work Item Types, and all visible states through Azure DevOps REST API 7.1. It writes explicit per-WIT state mappings into .ai-workflow.yml; runtime execution does not guess custom state meanings.

Role/provider assignments can be supplied during init:

./bin/ai-workflow init-ado \
  --organization <org> \
  --project <project> \
  --role-provider ArchitectAgent=claude \
  --role-provider TesterAgent=codex \
  --role-provider ImplementerAgent=codex

Validate later with:

./bin/ai-workflow doctor \
  --config .ai-workflow.yml \
  --discovery .ai-workflow.yml.ado-discovery.json

Doctor fails when ADO introduces a visible state that has no explicit mapping, when no ADO state maps to the canonical ready or completed state, or when required agent roles have no provider.

Plugin-style repository initialization

Existing repositories can install the workflow with one command:

./bin/ai-workflow init \
  --work-items github-issues \
  --environment codespaces \
  --codespaces-mode on-demand

For Azure DevOps:

AZURE_DEVOPS_TOKEN=... ./bin/ai-workflow init \
  --work-items azure-devops \
  --ado-organization <org> \
  --ado-project <project> \
  --environment codespaces \
  --role-provider ArchitectAgent=claude \
  --role-provider TesterAgent=codex \
  --role-provider ImplementerAgent=codex

The initializer updates only workflow-owned configuration sections and preserves unrelated custom YAML. It creates missing AGENTS.md, CURRENT.md, Codespaces devcontainer, and GitHub workflow caller stubs without overwriting existing project files unless --force is supplied. Doctor validation runs after init.

Guided setup wizard

For first-time installation, prefer the step-by-step setup flow over raw init flags:

GH_TOKEN=... ./bin/ai-workflow setup \
  --work-items github-issues \
  --github-repository owner/repo \
  --environment codespaces

For Azure DevOps:

AZURE_DEVOPS_TOKEN=... ./bin/ai-workflow setup \
  --work-items azure-devops \
  --ado-organization <org> \
  --ado-project <project> \
  --environment codespaces

Setup runs eight phases: repository context analysis, provider selection, current-status discovery, mapping confirmation, workflow policy, agent assignment, environment/credential guidance, then doctor/apply.

The setup context is exposed through a provider-neutral SetupAgent contract so an AI provider can recommend project-specific defaults after reading existing docs/tests/workflows. GitHub setup discovers existing labels and maps status-like labels; Azure DevOps setup discovers states per Work Item Type. Mapping is persisted explicitly and is never guessed during runtime.

Use --plan-only to generate .ai-setup-plan.yml without applying repository changes, or --non-interactive for automation.

AI provider registry

Agent roles reference provider IDs. Provider type, endpoint, model and authentication are configured separately.

Examples:

./bin/ai-workflow setup ... \
  --ai-provider codex=chatgpt-codex,model=gpt-5.6-codex \
  --ai-provider openai=openai,model=gpt-5.6,auth=api-key-env,env=OPENAI_API_KEY \
  --ai-provider claude=anthropic,model=claude-sonnet,auth=api-key-env,env=ANTHROPIC_API_KEY \
  --ai-provider litellm=litellm,base_url=http://litellm:4000/v1,auth=bearer-env,env=LITELLM_API_KEY \
  --ai-provider local=local-openai-compatible,base_url=http://host.docker.internal:11434/v1,model=qwen-local,auth=none \
  --role-provider ImplementerAgent=local

Supported provider types are openai, chatgpt-codex, anthropic, litellm, openai-compatible, local-openai-compatible, and interactive.

Authentication is an independent strategy: chatgpt-oauth, api-key-env, bearer-env, or none. Secrets are never written into project configuration; configuration stores only the environment variable name or interactive auth strategy.

Connection-first setup

Guided setup now connects the source host before repository/provider discovery.

GitHub authentication supports:

• gh-cli (default): reuse the authenticated GitHub CLI OAuth session from gh auth login;
• token-env: read a PAT/token from an environment variable such as GH_TOKEN.

SSH keys remain valid for Git fetch/push but are not used as GitHub API/WorkItem authentication.

Example:

gh auth login
./bin/ai-workflow setup \
  --github-auth gh-cli \
  --github-repository owner/repo \
  --work-items github-issues

The WorkItemProvider is selected from a registry. Built-ins are currently github-issues and azure-devops; core validation is registry-based so future providers can be added without changing canonical workflow states.

Azure DevOps MCP

When Azure DevOps is the WorkItemProvider, setup can also configure Microsoft's Azure DevOps MCP connection for agents.

Remote MCP is the default:

./bin/ai-workflow setup \
  --github-repository owner/repo \
  --work-items azure-devops \
  --ado-organization contoso \
  --ado-project App \
  --ado-mcp-mode remote \
  --ado-mcp-auth interactive

Local stdio MCP remains available with --ado-mcp-mode local. Supported MCP auth modes are interactive, azcli, env, envvar, and pat. Secrets are represented only by auth strategy/environment requirements and are never written to project configuration.

Worker registration

Setup can register local, Codespaces, or cloud workers separately from AI provider configuration. Worker IDs are independent from credential identities, so multiple workers may share one credential reference.

Example local hybrid worker:

./bin/ai-workflow setup ... \
  --worker desktop=hybrid,provider=local,environment=local,roles=ArchitectAgent|ImplementerAgent,assignee=my-github-login,planning_updates=approval-required

Example Codespaces executor:

./bin/ai-workflow setup ... \
  --worker cs=executor,provider=codex,environment=codespaces,roles=TesterAgent|ImplementerAgent,capacity=2

Planner/hybrid workers can connect directly to the configured WorkItemProvider (for example Azure DevOps MCP) to read current work items, analyze repository/work-item context, brainstorm, and propose updates. Planning mutations default to approval-required; frozen ready contracts cannot be silently changed. Executor/hybrid workers project into the existing queue-dispatcher worker registry and follow the reservation → provider accepted → in-progress lifecycle.

Complete setup documentation

For the full first-run and customization guide, see:

• docs/setup-guide.md — human-readable step-by-step setup, examples, credentials, providers, workers, ADO/GitHub, Codespaces/local and multi-repo.
• docs/setup-agent-playbook.md — canonical AI-readable SetupAgent playbook, invariants, approval boundaries and customization contract.

Recommended workflow:

AI or user reads setup guide/playbook
→ run setup --plan-only
→ review discovered status/WIT mappings and recommendations
→ approve/customize
→ run setup/apply
→ configure required secrets/sign-ins
→ doctor

The setup flow is customizable, but provider-specific states, credential values and frozen-contract changes must never be silently guessed.

Updating the framework

Use ./bin/ai-workflow update --check to inspect the configured framework ref, and ./bin/ai-workflow update --to <ref> to update framework-managed files and schema metadata. Project-owned configuration and files are preserved; locally modified managed files are skipped unless --force is explicitly used. See docs/setup-guide.md for the full update and migration rules.

Install from PyPI

Consumers do not need to clone this repository:

python -m pip install --upgrade ai-dev-workflow

After installation, start with the built-in guide:

ai-workflow guide

Typical first-run flow:

cd /path/to/project
ai-workflow setup
ai-workflow doctor

The setup wizard detects the containing Git repository by default, shows the generated plan/configuration, and prints the next required steps after apply. Use ai-workflow setup --help for setup options and ai-workflow guide at any time for the end-to-end usage flow.

The GitHub repository may remain private because normal consumers install the built distribution from PyPI rather than cloning the source repository.

On another machine, install Python 3.10+ and run the same pip install ai-dev-workflow command. Project-specific configuration remains in each consumer repository.

Framework CLI/package upgrades and project installation upgrades are separate:

python -m pip install --upgrade ai-dev-workflow
ai-workflow update

The first command upgrades the installed CLI package. The second migrates/updates the workflow installation in the current project.

PM and architecture planning

Product planning and technical architecture are separate roles:

Idea / chat
→ PMAgent
→ product Keep/Split/Merge proposal
→ human approval
→ ArchitectAgent
→ technical Spec / ADR / decomposition
→ approval for structural architecture changes
→ Ready Freeze
→ TesterAgent

PMAgent owns what/why: intent, value, scope, priority, product acceptance boundaries, Epic/Issue classification and product-level decomposition. ArchitectAgent owns how: technical boundaries, interfaces, data model, Spec/ADR, technical dependencies and execution decomposition.

By default workflow.planning.approval_policy is always. Create, Split, Merge, Re-parent and Ready Freeze require explicit approval before provider mutation, and frozen contracts cannot be silently changed.

Active work and claims

A selected task is not considered started until its worker/provider acknowledges it:

ready/queued
-> reserved
-> offered
-> accepted by worker/provider
-> claim committed
-> active status

Local agents acknowledge with the registered worker identity (for example through ai-workflow claim). Phase completion can be recorded with ai-workflow complete-phase, which releases the current claim and reserves an eligible worker for the next role. Automatic review always excludes the implementation worker_id; identity separation is enforced only when the optional strict-review policy is enabled.

Azure DevOps configuration boundary

Azure DevOps project metadata and credentials are separate:

• project configuration owns organization/project, logical Epic/Issue Work Item Type mappings, and Area/Iteration defaults;
• worker configuration owns the Azure DevOps authentication strategy/credential reference;
• setup may borrow a worker credential temporarily to discover project metadata, but never persists the raw token;
• workers may share a credential reference when they intentionally act as the same ADO identity.

Area Path and Iteration Path options are discovered from the project's existing Azure DevOps classification nodes. Setup selects from existing values; it does not create or modify the project's classification hierarchy.

Worker credentials and identity

Worker IDs and credentials are separate concepts. Several workers may intentionally use the same credential reference and therefore operate as the same GitHub/ADO actor. This is valid for normal planning, testing, implementation, and runtime work.

The workflow verifies effective actor identity where possible. A distinct credential is not required merely because workers are different. By default, ReviewerAgent only needs a different worker_id from the Implementer. Projects that require stronger separation may enable reviewer.require_distinct_identity: true.

Local parallel workers

One local machine may register multiple workers that intentionally share the same GitHub/Azure DevOps credential sessions while using isolated working folders.

Example:

workers:
  - worker_id: codex-01
    environment: local
    provider: codex
    workspace:
      strategy: git-worktree
      root: G:\ai-workers\codex-01

  - worker_id: ccr-01
    environment: local
    provider: ccr
    workspace:
      strategy: git-worktree
      root: G:\ai-workers\ccr-01

Add another local worker after initial setup:

ai-workflow add-worker

or non-interactively:

ai-workflow add-worker `
  --worker-id codex-02 `
  --provider codex `
  --mode hybrid `
  --workspace G:\ai-workers\codex-02 `
  --workspace-strategy git-worktree

By default the new local worker inherits credential references from an existing local worker. It still receives its own worker_id and isolated workspace. Use --no-inherit-credentials when the worker must use a different identity.

At runtime, a local worker is resolved in this order:

1. explicit --worker <id>;
2. AI_WORKFLOW_WORKER_ID;
3. current working directory contained by one configured workspace.root.

This makes it possible to open separate terminals in separate worker folders and run tasks concurrently without repeating the worker ID.

Claude Code Router local provider

Claude Code Router can be registered as a local provider:

providers:
  ccr:
    type: claude-code-router
    base_url: http://127.0.0.1:3456
    auth:
      type: none

The gateway defaults to http://127.0.0.1:3456. A client-key environment reference can be configured when the local router is protected. Claude Code Router uses the same CLAUDE.md instruction adapter as Claude Code, while AGENTS.md remains the shared workflow instruction source.

A local CCR worker can then be registered with its own workspace:

ai-workflow add-worker `
  --worker-id ccr-01 `
  --provider ccr `
  --workspace G:\ai-workers\ccr-01

Step-by-step add-worker wizard

Running ai-workflow add-worker with no --non-interactive flag now opens a structured wizard:

1. review existing project/provider configuration;
2. choose a worker id;
3. choose planner/executor/hybrid mode;
4. choose an existing AI provider;
5. choose roles;
6. choose the local workspace folder;
7. choose git-worktree or folder workspace strategy;
8. choose whether to inherit an existing local worker's credential references or configure separate GitHub/ADO references;
9. choose skills and capacity;
10. review the final worker configuration and confirm before saving.

The wizard never stores raw tokens. It only stores credential strategies/references already supported by the worker identity model.

For scripting/automation, keep the deterministic flag-based flow:

ai-workflow add-worker `
  --non-interactive `
  --worker-id codex-02 `
  --provider codex `
  --mode hybrid `
  --roles "ArchitectAgent|TesterAgent|ImplementerAgent|ReviewerAgent" `
  --workspace G:\ai-workers\codex-02 `
  --workspace-strategy git-worktree

Use --no-inherit-credentials when the new worker must not reuse an existing local worker's credential references.

Consumer worker registry and live presence

When a GitHub Issues or composite project has execution workers, setup writes:

.github/ai-workflow-worker-registry.json

This committed registry contains routing metadata only:

• worker id;
• execution location;
• provider id;
• roles/capabilities;
• capacity;
• managed/unmanaged flag;
• GitHub assignee when required.

It intentionally excludes machine-local paths, machine ids, client launch commands, credential references, tokens, identities, heartbeat data, and active sessions.

For local execution workers, setup also creates or reuses one closed coordination Issue marked with:

<!-- ai-dev-workflow:worker-presence -->

The Issue number is stored as project-level dispatcher metadata. Local workers publish the existing queue-dispatcher presence-v2 contract to that Issue using their own verified GitHub identity.

ai-workflow worker open <worker-id> keeps the local and central heartbeat alive while the configured Codex/Claude Code client process is running. When the client exits, the worker is reported offline.

For headless or daemon-style workers:

ai-workflow worker serve <worker-id>

A worker keeps one presence comment and updates it on each heartbeat; it does not append a new comment every interval. The central dispatcher reads the consumer registry plus live presence before reserving work.

The consumer orchestration workflow remains .github/workflows/ai-workflow.yml. This integration does not install framework CI/check workflows, does not configure required status checks, and does not modify branch protection.