---

The Problem

AI agents are eager to help. Too eager.

Ask one to fix a bug and it starts editing code immediately - before understanding the system, before considering alternatives, before verifying assumptions. It's not stupid; it's a predictive model doing what predictive models do: fill in gaps and race to an answer.

You can add system prompts or skills: "plan before coding," "gather context first," "follow our architecture guidelines." But system prompts fade as conversations grow. Skills front-load all guidance at once - which works for simple tasks but breaks down when the task is long and the guidance is complex. The agent reverts to its default: assume, predict, jump to conclusions.

The deeper problems compound from there:

• Tasks left incomplete - the agent ships something that looks done but skips the hard parts
• Guidelines ignored - your architecture rules, best practices, and team conventions aren't enforced; the agent knows them but doesn't apply them
• No audit trail - when AI work goes wrong, there's no record of what decisions were made or why
• Context lost between sessions - every new conversation starts from zero; prior work, decisions, and context vanish
• Parallelism is chaos - running multiple AI tasks simultaneously means constant context-switching and re-explaining; there's no shared structure

The result: inconsistent quality that depends on how much you babysit the agent.

---

How WorkRail Works

WorkRail replaces the human effort of guiding an agent step-by-step.

Instead of one system prompt that fades over time, WorkRail drip-feeds instructions through the Model Context Protocol. The agent calls start_workflow, gets the first step, completes it, calls continue_workflow. Future steps stay hidden until previous ones are done.

The agent can't skip to implementation because it doesn't know those steps exist yet.

Sessions are durable. Work is saved to disk at every step and can be resumed across conversations with resume_session — pick up exactly where you left off, even days later or in a fresh chat.

The Mechanism

You                      Agent                     WorkRail
 │                         │                          │
 │  "Fix the auth bug"     │                          │
 │────────────────────────>│                          │
 │                         │                          │
 │                         │  start_workflow()        │
 │                         │─────────────────────────>│
 │                         │                          │
 │                         │   Step 1: Understand     │
 │                         │      the problem         │
 │                         │<─────────────────────────│
 │                         │                          │
 │   "What error do you    │                          │
 │    see exactly?"        │                          │
 │<────────────────────────│                          │
 │                         │                          │
 │         ...             │  continue_workflow()     │
 │                         │─────────────────────────>│
 │                         │                          │
 │                         │   Step 2: Plan your      │
 │                         │      investigation       │
 │                         │<─────────────────────────│

Without WorkRail

You:   "There's a bug in the auth flow"

Agent: "I see the issue! In auth.js line 42, there's a null check that 
        should handle this. Let me fix it..."
        
        *edits code based on a 30-second skim*
        *breaks something else*

With WorkRail

You:   "There's a bug in the auth flow"

Agent: "I'll use the bug-investigation workflow."
        → start_workflow()
       
       Step 1: Investigation Setup
       "Before I investigate, I need to understand the problem.
        What exactly happens when it fails? Can you share the error?"
       
       [Documents bug, reproduction steps, environment]
        → continue_workflow()
       
       Step 2: Plan Investigation
       "I'll trace execution from login through the auth middleware.
        Key areas: token validation, session lookup, error handling."
       
       [Creates investigation plan before touching code]
        → continue_workflow()
       
       Step 3: Form Hypotheses
       "Based on my analysis, three possible causes:
        H1: Clock skew in token validation (7/10)
        H2: Race condition in session lookup (6/10)
        H3: Null check masking the real error (4/10)"
       
       [Tests hypotheses systematically, gathers evidence, proves root cause]

Same agent. Same model. But it prepared properly because it had no choice.

Why Steps Are Structured This Way

Each step follows a pattern that prevents common AI failure modes:

• Prep: Understand before acting - read the code, clarify requirements, confirm approach
• Implement: One focused change - not five things at once
• Verify: Validate before continuing - catch errors before they compound

This isn't arbitrary structure. It's how experienced developers actually work.

Durable Sessions

Sessions persist to disk at every step. Close the chat, come back tomorrow, pick up exactly where you left off:

# New conversation, days later
> "Resume the auth refactor I was working on"

Agent: → resume_session()

WorkRail: Found your session from 3 days ago.
          You were on Step 4: Implement token rotation.
          Here's what you had documented so far...

Each step's output — notes, decisions, artifacts — is saved and concatenated automatically. No context re-setup. No re-explaining what was already done.

Visibility and Audit Trail

The WorkRail Console is a browser dashboard that shows every active and completed session. It auto-boots when you use WorkRail and gives you a live view of what the agent is doing, what it has done, and what decisions it made at each step.

Open it anytime with worktrain console.

Running Tasks in Parallel

Because sessions are independent and durable, you can run multiple AI tasks simultaneously without babysitting any of them. Start five workflows, let each agent work through its steps, check in when they checkpoint. No context-switching overhead — each session has its own complete state.

Why This Beats System Prompts

System Prompt / Skill	WorkRail
"Plan first" fades as context grows	Each step is fresh and immediate
Agent decides what to follow	Agent can't skip - next step is hidden
Skills front-load all guidance at once	Guidance is delivered one step at a time, in context
One-size-fits-all instructions	Workflows encode your team's rules and best practices
Inconsistent results	Repeatable, consistent quality
Stateless — context lost when chat ends	Durable sessions — resume exactly where you left off
One task at a time or constant context-switching	Independent sessions run in parallel without babysitting

---

Quick Start

Add to your MCP client config (Claude Code, Cursor, Firebender, Antigravity, etc.):

{
  "mcpServers": {
    "workrail": {
      "command": "npx",
      "args": ["-y", "@exaudeus/workrail"]
    }
  }
}

Then prompt your agent:

"Use the bug-investigation workflow to debug this auth issue"

The agent will find the workflow, start at step 1, and proceed systematically.

Troubleshooting: "Permission denied" on startup

Versions before 3.19.0 were published without the execute bit set on the binary. If you see Permission denied when WorkRail starts, reinstall or fix it in place:

# Option A: reinstall (recommended)
npm install -g @exaudeus/workrail

# Option B: fix in place without reinstalling
chmod +x $(npm root -g)/@exaudeus/workrail/dist/mcp-server.js

---

CI & Releases

• Lockfile is enforced: package-lock.json is canonical and CI will fail if npm ci would modify it. Commit lockfile changes intentionally.
• Release authority: releases are produced by semantic-release in GitHub Actions (don’t bump versions/tags locally).
• Major releases are approval-gated: breaking changes become minor by default and only become major when WORKRAIL_ALLOW_MAJOR_RELEASE=true.
• Release type comes from the commit on main: for squash merges, the PR title / squash commit title controls whether the release is patch, minor, major, or untagged. See docs/reference/releases.md.
• Preview a release (dry-run):
  • Locally: npx semantic-release --dry-run --no-ci
  • Locally (major allowed): WORKRAIL_ALLOW_MAJOR_RELEASE=true npx semantic-release --dry-run --no-ci
• In Actions: run the Release (dry-run) workflow (.github/workflows/release-dry-run.yml).
• Full release policy: see docs/reference/releases.md

---

Included Workflows

30+ workflows included for development, debugging, review, documentation, and more:

Workflow	When to Use
coding-task-workflow-agentic	Feature development with notes-first durability and audit loops
bug-investigation.agentic.v2	Systematic debugging with evidence-based analysis
mr-review-workflow.agentic.v2	Code review with parallel reviewer families
wr.discovery	Upstream exploration, framing, and design synthesis
wr.shaping	Shape a fuzzy problem into an implementation-ready pitch
document-creation-workflow	Technical documentation with structure
architecture-scalability-audit	Audit a service for production readiness and scalability

Workflows adapt to complexity - simple tasks get fast-tracked, complex tasks get full rigor.

See all workflows →

---

The Philosophy

Guardrails Enable Excellence

WorkRail doesn't lobotomize your AI. The agent still reasons, explores, and creates - but within a structure that ensures it actually prepares, plans, and verifies. Guardrails prevent shortcuts, not creativity.

Expert Knowledge, Codified

Workflows aren't just task checklists. They embed hard-won expertise: "verify understanding before implementing," "form multiple hypotheses before concluding," "test assumptions with evidence." This is how senior engineers think - now encoded into every workflow.

Replacing the Human Guide

A skilled developer doesn't let AI run unsupervised on complex tasks. They guide it: "Wait, did you check X?" "What about edge case Y?" "Show me your reasoning."

WorkRail does this automatically. The workflow asks the questions a senior dev would ask, at the moments they'd ask them.

Team Knowledge, Shared

Workflows are JSON files you can version-control and share with your team. Encode your architectural rules, review checklists, and best practices directly into the execution path — so every AI task on your team follows the same standards, automatically.

A workflow you write for your own team is a workflow that runs the same way for every engineer on it.

---

Create Your Own

Drop a JSON file in ~/.workrail/workflows/:

{
  "id": "my-review-checklist",
  "name": "Team Code Review",
  "version": "1.0.0",
  "description": "Our standard review process",
  "steps": [
    {
      "id": "check-tests",
      "title": "Verify Test Coverage",
      "prompt": "Check that new code has tests. List untested paths.",
      "agentRole": "You are a reviewer focused on test coverage."
    },
    {
      "id": "check-security",
      "title": "Security Review",
      "prompt": "Look for: injection risks, auth issues, data exposure.",
      "agentRole": "You are a security-focused reviewer."
    }
  ]
}

WorkRail discovers it automatically. This is a minimal example - workflows also support conditions, loops, validation criteria, and more.

Writing workflows → · Load from Git →

---

Documentation

• All Workflows – Full list with detailed descriptions
• Writing Workflows – Custom workflow creation guide
• Workflow Authoring Principles (v2) – Cross-workflow design rules and authoring principles
• Configuration – Git repos, environment variables, local paths
• Advanced Features – Loops, conditionals, validation
• Integrations
  • Claude Code Setup – Complete guide for Desktop & CLI
  • Firebender Setup – Firebender integration
• Console & Sessions
  • Run worktrain console to open the browser dashboard
  • Use resume_session in any conversation to reconnect to an existing session

---

GitHub · MIT License