Auto mode is a **state machine driven by files on disk**. It reads `.sf/STATE.md`, determines the next unit of work, creates a fresh agent session with pre-loaded context, and lets the LLM execute. When the LLM finishes, auto mode reads disk state again and dispatches the next unit.
Plan → Execute (per task) → Complete → Reassess Roadmap → Next Slice
↓ (all slices done)
Validate → Complete Milestone
```
- **Plan** — scouts the codebase, researches docs, decomposes the slice into tasks
- **Execute** — runs each task in a fresh context window
- **Complete** — writes summary, UAT script, marks roadmap, commits
- **Reassess** — checks if the roadmap still makes sense
- **Validate** — reconciliation gate after all slices; catches gaps before sealing the milestone
## Key properties
### Fresh session per unit
Every task, research phase, and planning step gets a clean context window. The dispatch prompt includes everything needed — task plans, prior summaries, dependency context, decisions register — so the LLM starts oriented.
### Context pre-loading
| Inlined artifact | Purpose |
|------------------|---------|
| Task plan | What to build |
| Slice plan | Where this task fits |
| Prior task summaries | What's already done |
| Dependency summaries | Cross-slice context |
| Roadmap excerpt | Overall direction |
| Decisions register | Architectural context |
The amount of context inlined is controlled by your [token profile](/guides/token-optimization). Budget mode inlines minimal context; quality mode inlines everything.
A lock file tracks the current unit. If the session dies, the next `/sf auto` synthesizes a recovery briefing from tool calls that made it to disk and resumes with full context.
A sliding-window analysis detects stuck loops — catching cycles like A→B→A→B as well as single-unit repeats. On detection, SF retries once with a diagnostic prompt. If it fails again, auto mode stops with the exact file it expected.
SF maintains a `KNOWLEDGE.md` file — an append-only register of project-specific rules, patterns, and lessons learned. The agent reads it at the start of every unit and appends when discovering recurring issues or non-obvious patterns.
After milestone completion, SF auto-generates a self-contained HTML report with progress tree, dependency graph, cost/token metrics, execution timeline, and changelog.
When `reactive_execution: true` is set, SF derives a dependency graph from IO annotations in task plans. Tasks that don't conflict are dispatched in parallel via subagents.
