How Draft Prevents Agent Conflicts in Parallel Execution
The Conflict Problem
When multiple AI agents work on the same codebase simultaneously, conflicts are inevitable — unless you design around them. Two agents editing the same file, one agent breaking another's assumptions, or incompatible changes merging into a broken state. This is the central challenge of multi-agent development.
Traditional approaches either serialize agent work (slow) or hope for the best with post-hoc merging (unreliable). Draft solves this architecturally.
DAG-Based Dependency Planning
Draft uses a directed acyclic graph (DAG) to analyze your feature description and determine task dependencies. When you describe a feature like "add user authentication with login page, API endpoint, and database migration," Draft identifies:
- Database migration must complete before the API endpoint can reference the new tables
- The API endpoint and login page can execute in parallel since they touch different files
- Integration tests depend on both the API and frontend being complete
This dependency graph ensures agents never step on each other's work. Parallel execution happens only where it is safe.
Isolated Branch Execution
Each agent operates on an isolated git branch scoped to its specific subtask. Agent A working on the database migration has no visibility into Agent B's frontend changes. This isolation eliminates an entire class of conflicts: no shared mutable state, no concurrent file edits, no assumption violations.
Verified Merge
When agents complete their subtasks, Draft's merge engine combines the results in dependency order. At each merge step, automated verification runs: tests, type-checking, and linting. If any step fails, Draft retries with additional context from the failure output rather than passing broken code forward.
The result is a single, clean pull request with no merge conflicts and no broken tests — even when five agents contributed to it.
Why This Matters
Without conflict prevention, parallel agent execution is a liability. More agents means more potential for breakage, more time spent resolving conflicts, and less confidence in the output. With DAG-based planning and isolated execution, more agents means faster feature delivery with the same reliability guarantees.
This is the core technical insight behind Draft: parallelism is only valuable if correctness is preserved.