Longitudinal case study validates DEVA methodology for AI agent-assisted software engineering, suggesting improvements in development speed.
The rapid adoption of AI coding assistants has revealed a structural problem: experienced developers using frontier models without structured methodology are measurably slower than without assistance. We present DEVA (Delegation with Adversarial Verification), a four-phase methodology for building production-grade software with AI agents: instruction Design, agentic Execution, gate Verification, and friction Audit. We validate DEVA through a longitudinal case study: a 28-module enterprise system (94 database tables, 46 cumulative regression steps never broken, 49 payroll golden cases with zero tolerance deviation). The dual integral audit produced zero critical findings. The methodology is tool-agnostic and model-agnostic by construction.
No takes yet. Share an insight, caveat, or question.
Germán Ontiveros (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: