What problem does it solve? Long-running AI-agent projects lose state when task tracking lives only in chat history, and execution quality suffers when planning, implementation, and review share one session. Railyard separates Planner, Architect, Runner, and Validator roles with durable SQLite workflow state so work survives across disposable sessions. ## Core Features & Use Cases - Role-based workflow protocol: Human, Planner, Architect, Runner, and Validator roles with explicit lane separation (Domain and System), ticket lifecycle rules, and closed-loop Architect review of Runner results. - Durable SQLite state: Epics, tickets, claims, results, and reviews persist in a local workflow database, with helper scripts for dispatch, claim, stale-ticket recovery, and review recording. - Deterministic validation: Validation contracts, a Validator gate on tickets, a failure taxonomy for blockers, and a v0.8 local runtime with event journal, gate decisions, action policy, and a 20-scenario smoke suite. - Use Case: A team running multi-session agent development clones Railyard into their project, initializes the workflow database, and has a Planner decompose work into epics and tickets that Architects dispatch to Runner sessions with independent Validator evidence before acceptance. ## Quick Start Clone the repository into your project as a railyard subdirectory, run python railyard/scripts/init_workflow.py --project-root ., then start a Planner session asking it to read railyard/SKILL.md and convert your project direction into epics and tickets.