1. Scope
1.1 Purpose
SENAR defines a methodology for software development where AI agents are the primary producers of engineering artifacts and human engineers serve as Supervisors — directing, verifying, and governing the AI-driven process.
1.2 Intended Audience
- Organizations transitioning to AI-native software development;
- Supervisors — engineers who direct AI agents;
- Managers responsible for delivery, quality, and cost;
- Tool vendors building AI-native development platforms;
- Auditors evaluating quality practices of AI-native teams.
1.3 Applicability
This standard applies where:
a) AI agents generate a substantial portion of production artifacts (code, tests, configuration, documentation); b) Human engineers direct, review, and approve AI-generated output; c) Traceability from requirements to delivered artifacts is required; d) Quality assurance is enforced through automated mechanisms.
SENAR is tool-agnostic. It applies to any AI coding platform — autonomous agents, IDE assistants, terminal-based tools, or custom pipelines.
1.4 Out of Scope
a) AI model training, fine-tuning, or evaluation; b) Traditional development where humans write the majority of code; c) Organizational change management; d) Specific tool implementations.
1.5 Relationship to Other Standards
SENAR extends and adapts concepts from established methodologies:
- SAFe 6.0 — SENAR reimagines SAFe concepts for AI-native teams. SAFe comparison notes are provided throughout the document.
- ISO 9001:2015 — SENAR Quality Gates support selected ISO 9001 clauses. Organizations should conduct gap analysis for full compliance.
- ISO/IEC 25010:2023 — SENAR metrics align with the software quality model.
- Scrum / Kanban — SENAR borrows iterative delivery and flow measurement while replacing human-centric ceremonies with AI-appropriate alternatives.
SENAR may be extended with domain-specific profiles for regulated industries (medical devices, financial services, aerospace) that add controls required by sector-specific standards.