SAW — SAFe Agentic Workflow

AI Agent Harness for Multi-Agent Team Workflows

A Production-Tested Three-Layer Architecture for Coordinated AI Teams

Supported AI Providers

Template Repository - Click "Use this template" above to create your own AI agent harness. After cloning, run bash scripts/setup-template.sh to customize for your project. See TEMPLATE_SETUP.md for details.

What This Is

A production-tested AI agent harness for teams that want structured AI workflows.

Multi-provider support: Works with Claude Code (Anthropic), Gemini CLI (Google), Codex CLI (OpenAI), and Cursor IDE (Anysphere).

Built on SAFe methodology (Scaled Agile Framework), adapted for AI agent teams. Works for any team with repeatable processes: Software, Marketing, Research, Legal, Operations.

Includes:

• 20 Model-Invoked Skills - Domain expertise that loads automatically (Skills 2.0 frontmatter)
• 24 Slash Commands - Workflow automation for common tasks
• 11 SAFe Agent Profiles - Specialized roles with clear boundaries
• Three-Layer Architecture - Hooks → Commands → Skills
• Agent Teams - Multi-agent orchestration with SAFe quality gates (experimental)
• Dark Factory - Persistent autonomous agent teams via tmux on remote servers (guide)
• Knowledge Vault - Evidence-verified knowledge base with a drift-detecting validator (guide)

Origin: 5 months production use, 169 issues, 2,193 commits. Implements patterns from 6 Anthropic engineering papers and SAFe methodology.

Quick Start (30 seconds)

Claude Code (Anthropic)

# Copy harness to your project
cp -r .claude/ /your-project/.claude/

# Customize placeholders across all provider files ({{TICKET_PREFIX}}, {{PROJECT_NAME}},
# and the rest) in one pass:
bash scripts/setup-template.sh

# Start working
/start-work TICKET-123

Gemini CLI (Google)

# Copy harness to your project
cp -r .gemini/ /your-project/.gemini/

# Install Gemini CLI (if needed)
npm install -g @google/gemini-cli

# Authenticate
export GEMINI_API_KEY="your-api-key"

# Start working
/workflow:start-work TICKET-123

Codex CLI (OpenAI)

# Copy harness to your project
cp -r .codex/ /your-project/.codex/
cp -r .agents/ /your-project/.agents/

# Install Codex CLI (if needed)
npm install -g @openai/codex

# Authenticate
export OPENAI_API_KEY="your-api-key"

# Start working (natural language, no slash commands)
codex

Cursor IDE (Anysphere)

# Copy rules to your project
cp -r .cursor/ /your-project/.cursor/

# Open in Cursor
cursor /your-project

# Rules activate automatically based on file context
# Use @rule-name to invoke agent roles manually

That's it. Your AI assistant now has your team's workflow patterns built in.

Keeping Your Harness Updated

Already using the harness and a new version is out? You have two paths:

Automated (multi-domain, manifest-based):

# Initialize sync metadata (first time only)
./scripts/sync-claude-harness.sh init
./scripts/sync-claude-harness.sh manifest init --yes

# Preview and apply (syncs all domains in your manifest's sync_scope)
./scripts/sync-claude-harness.sh sync --version v2.11.1 --dry-run
./scripts/sync-claude-harness.sh sync --version v2.11.1

# Sync specific domains only
./scripts/sync-claude-harness.sh sync --version v2.11.1 --scope .claude,.gemini

Manual (full release, all providers):

git remote add harness https://github.com/bybren-llc/safe-agentic-workflow.git
git fetch harness main --tags
git diff v2.10.0..v2.11.1 --stat             # See what changed
git checkout harness/main -- .codex/agents/   # Cherry-pick what you need
bash scripts/sync-claude-harness.sh --dry-run # Preview, then drop --dry-run to apply

The sync script protects your customizations via a manifest (required since v2.10.0). It won't overwrite files you've marked as protected. See the Harness Sync Guide for the full reference and Upgrade Guide for rollback options.

The Three-Layer Architecture

┌──────────────────────────────────────────────────────────────────────┐
│                      Claude Code Harness                              │
├──────────────────────────────────────────────────────────────────────┤
│  LAYER 1: HOOKS     │ Automatic guardrails (format checks, blockers) │
│  LAYER 2: COMMANDS  │ User-invoked workflows (/start-work, /pre-pr)  │
│  LAYER 3: SKILLS    │ Model-invoked expertise (pattern discovery)    │
└──────────────────────────────────────────────────────────────────────┘

Philosophy: Process as service, not control. Everything exists to reduce cognitive load on already-solved problems.

Choose Your Path

Gemini CLI Integration

# Installation
npm install -g @google/gemini-cli

# Authentication (choose one)
export GEMINI_API_KEY="your-api-key"
# or
gcloud auth application-default login

# Start Gemini CLI
gemini

# List available commands
/help

# List available skills
/skills

Implementing Anthropic's Research

This harness directly implements patterns from Anthropic's engineering papers:

"The best harness is one you forget exists." — Agent Perspective

SAFe Foundation

Program Cadence: SAFe x AI-DLC

SAFe gives this harness its structure. But SAFe's cadence assumes human squads on week-long sprints, and agent teams do not move at that speed — a team of specialized agents can elaborate, build, and verify a unit of work in hours.

So the harness also ships the SAFe x AI-DLC fusion: SAFe keeps the hierarchy, WSJF, role boundaries, and Definition of Done; AWS's AI-Driven Development Life Cycle supplies the cadence and the human checkpoint. In a program that adopts the fusion, the Bolt takes the sprint's place. Adoption is per-program; the standard sprint path stays valid.

Inside such a program, each concept below stands in for its SAFe counterpart:

The human validation step is not optional. Agents own the build; humans own the judgment — secrets, security policy, branch protection, risk thresholds, and signing the Definition of Done always route to a human with options and a recommendation.

Using It

Use it when work spans many issues and needs cadence — turning an audit, epic, or initiative into an executable program. For a single ticket, the standard safe-workflow path is correct. And if the problem space is still unclear, run a spike instead: forcing an ambiguous epic into one Bolt just relocates the ambiguity into the code.

Knowledge Vault

Agent teams need a shared map of the system, and a map nobody can prove is current will quietly become wrong. The knowledge-vault/ subsystem is an evidence-verified knowledge base: every concept records the commit its claims were checked against, so staleness is something you compute, not something you feel.

Built on Open Knowledge Format v0.1 (Google, Apache-2.0), which gives portability. This harness adds the rigor layer that gives trust: a strict frontmatter contract, a zero-dependency validator, an anti-hallucination link rule, and a drift mechanism.

In the project this method came from, an independent architecture audit called the vault "the single strongest KT asset in the repo" and told new developers to trust it over the project's own canonical context file — because the vault's claims were verified against a SHA and the canonical file's had silently drifted.

Run It

# Prove the tooling works before you trust it
node knowledge-vault/scripts/validate-vault.mjs --vault knowledge-vault/templates/starter-bundle

The reading layer

Because an OKF bundle is a directory of plain markdown, Obsidian opens it with no conversion step, and that is where the vault stops feeling like a docs folder: a graph view of the concept graph (colour-grouped by directory, with orphans deliberately visible because an orphan is a defect), canvases for relationships a linear document cannot show, and Bases saved queries including a drift dashboard listing every concept whose verified_against has fallen behind. knowledge-vault/templates/obsidian/ ships the app, graph and core-plugin config; the canvases and Bases views ship inside the vault itself. Bases needs Obsidian 1.9+.

Obsidian is not required: no community plugins are needed, and the vault degrades to plain markdown in any editor. But the graph, canvases and dashboard are a large part of what you get.

The 11-Agent Team

See AGENTS.md for complete reference with invocation examples.

Domain Adaptation Guide

The harness patterns work beyond software engineering:

Marketing Team Example

Research Team Example

What Makes This Different

Round Table Philosophy

Human and AI input have equal weight. No hierarchy, just expertise.

Stop-the-Line Authority

Any agent can halt work for architectural or security concerns.

Pattern Discovery Protocol

"Search First, Reuse Always, Create Only When Necessary"

Evidence-Based Delivery

All work requires verifiable evidence. No "trust me, it works."

vNext Workflow Contract (v1.4)

Note from the Author: It became apparent early on that some of the autonomy and alignment we'd lost in our original harness was not going to work. This re-introduces strong solo and larger orchestration hats with selection criteria. Gates for QAS cover all scenarios.

Complete Agent Flow

┌─────────────────────────────────────────────────────────────────────────────────────────┐
│                        SAFe AGENTIC WORKFLOW - vNext                                     │
└─────────────────────────────────────────────────────────────────────────────────────────┘

                                    ┌──────────────┐
                                    │  USER/POPM   │
                                    │  Creates     │
                                    │  Linear      │
                                    │  Tic