How is RailRun different from Linear or Jira?

Tickets tell a human what to build; a RailRun runbook tells an AI agent how to build it — with checkpoints, shared knowledge and an audit trail. RailRun reads from Linear/Jira; it does not replace them.

Is RailRun the same as Factory.ai, Devin or Cognition?

No. Those replace the engineer's agent with an autonomous one. RailRun coordinates whatever agent the team already uses (Claude Code and Cursor) and adds human checkpoints, governance and audit on top.

Why not just use SKILL.md or Copilot Skills?

A skill file tells one agent how to do one task. RailRun governs a whole team of agents with versioned runbooks, human approval, AI-vs-human audit and per-seat billing — things a text-file format structurally cannot provide, and it is vendor-neutral across MCP hosts.

Is RailRun a coding agent?

No. RailRun writes no code. Your agent (Claude Code or Cursor) brings the hands; RailRun brings the rails — the shared process, checkpoints and audit layer above it.

Runbooks for agentic coding · 2026

Linear ships tickets.
Factory ships droids.
RailRun ships runbooks.

The shared runbook layer for any engineering team using AI agents. Design a template once on the canvas — every engineer reruns the same bounded workflow through whichever agent they prefer (Claude Code or Cursor), with human checkpoints and an audit trail your tech lead can actually trust.

Start free See the stack

Tickets · roadmaps · sprints

LINEAR / JIRA / NOTION

Workflow templates · checkpoints · audit

Agents that actually write the code

CLAUDE CODE / CURSOR

Vendor MCPs — data and actions

JIRA · FIGMA · GITHUB · SLACK · FIREBASE

The shape of the product

A shared runbook your agents can follow.

You don't write code for your integrations. You draw them on a canvas. A trigger, a chain of steps, a human approval, an outcome — saved as a template your whole team can re-run on any ticket, through any agent. That's what mobile teams have been missing from AI-assisted SDLC.

BUG FIX RUNBOOK · live

Trigger · new Jira ticket in PROD-BUGS

JIRA

Scope · app/src/main/** · read ticket context

RAILRUN

Agent · Claude Code or Cursor generates diff

YOUR LLM

Checkpoint · tech lead approves plan

HUMAN

Build · <your build command> · tests green

LOCAL

PR · open · label railrun-generated

GITHUB

Deliver · <your distribution target> + Slack notify

For every engineering team

Nodes surface based on what your agent detects.

You don't configure stacks up front. Your agent reads package.json, build.gradle, Cargo.toml — whatever it finds — and reports back. The canvas palette reshapes. Starter runbooks for the detected stack are pre-wired, so you're running one minutes after connecting. No picker, no dropdown, no guessing.

MOBILE

Android · iOS · KMP · Flutter · RN

WEB

React · Vue · Svelte · Next · Astro

BACKEND

Node · Python · Go · Rust · Java

CROSS-PLATFORM

Monorepos · infra · data · ML

Starter runbooks for the detected stack: Crash-to-PR, Figma → your framework, Dependency bump, REST endpoint, Database migration, Beta release, Infra change. Fork any on the canvas. Share with your team.

Three approaches

Different layers, not competing products

Every sensible engineering stack in 2026 will have all three. The question is who owns the seam between them.

Linear

Issue tracker · roadmap

Best-in-class ticketing. Rich roadmaps, cycles, triage. Their new AI adds ticket drafting and sub-issue generation — but execution still lives somewhere else.

StrongTicket lifecycle, sprint planning, roadmap view
WeakDoesn't execute code changes. No agent orchestration.
BuyerProduct managers + team leads
LayerSystem of record for what needs to happen

Factory.ai

Autonomous droids

"Drop-in AI engineers." Their droids run multi-step dev tasks end-to-end autonomously on their own infrastructure. Fast for self-contained work, but locked to their platform and their model choices.

StrongAutonomous execution on well-scoped tasks
WeakSingle-agent (theirs). No team roles, no shared templates, no BYOK.
BuyerSolo engineers, small teams willing to hand off control
LayerA specific agent implementation

RailRun

Workflow layer · MCP-native

The conductor between your tickets and your agents. Define repeatable templates — 7 phases, configurable nodes, human checkpoints — then run them from whatever agent each engineer prefers. One workflow across Claude Code and Cursor.

StrongTemplates, checkpoints, role-based admin, audit trail
Works withClaude Code and Cursor — via MCP
BuyerEngineering managers + tech leads who need team visibility
LayerThe repeatable workflow above any agent

The agentic coding stack

Four layers. We live in the one nobody else owns.

Every layer has a vendor. Only the workflow layer is empty — everyone assumes either ticketing or the agent should cover it. Neither does.

L4 · Intent

Tickets and intent

Linear · Jira · Notion · GitHub Issues · Shortcut

System of record

L3 · Workflow

Templates, phases, checkpoints, audit

RailRun · (nobody else)

That's us

L2 · Agent

The thing that actually writes code

Claude Code · Cursor

LLM + orchestrator

L1 · Tools

MCP servers — data and actions

Jira MCP · Figma MCP · GitHub MCP · Slack MCP · Firebase MCP · shell · filesystem · git

Vendor nouns

Honest comparison

Who covers what

Every tool here is excellent at what it does. This matrix is about scope, not quality — where do capabilities actually live?

Capability	Linear	Factory.ai	Cursor / Copilot	RailRun
Tickets + roadmap		—	—	INT
Agent executes code changes	—			VIA
Reusable workflow templates	ISSUE	DROID	—
Human-in-the-loop checkpoints	—	SOME	—
Works with any agent (BYOK hosts)	—	—	—
Team roles + multi-party approval		—	—
Immutable audit trail	LOG	—	—
Stack-agnostic workflow layer (any agent, any stack)	—	—	—

✓ full support · INT via integration · — not in scope

Why this layer, why now

The four gaps we fill

Repeatable across your team

Cursor and Factory give your best engineer a 10× boost — and leave four others guessing. RailRun templates encode "how our team ships a bug fix" — or a feature, or a migration — so every engineer gets the same quality path, the same checkpoints, the same review standards.

Human checkpoints, by construction

Autonomous agents race to a PR. Regulated teams can't ship that way. RailRun templates pause at phase boundaries you define — plan approved, scope approved, diff approved — and resume when a reviewer clicks. Safety isn't a setting, it's in the template.

Your agent, not ours

Claude Code or Cursor — whatever each engineer is paying for. RailRun runs as an MCP server; the host calls our tools to orchestrate. No forced agent, no forced LLM, no forced IDE. You bring the hands.

Audit that holds up in review

Every phase transition, every checkpoint decision, every LLM call summary lands in an immutable, hash-chained event log. Your compliance team sees the exact history. Your tech lead sees who approved what. Your PM sees velocity. Same source of truth.

The ecosystem

One workflow, every vendor's MCP

Vendors ship MCPs for nouns — "read this Jira ticket," "fetch this Figma frame," "open this PR." RailRun is the verb that conducts them in the right order.

RAILRUN
WORKFLOW MCP

Jira

Linear

GitHub

Figma

Firebase

Slack

Shell

Git

Sentry

Notion

Play

One RailRun key · every vendor's MCP · every host's agent

Linear ships tickets. Factory ships droids. RailRun ships runbooks.