RayMish Frames Full Agentic SDLC
RayMish’s Agentic SDLC model places AI agents across planning, requirements, architecture, coding, testing, security, deployment, and observability, with humans setting intent and governing execution. RayMish describes this broader approach as AI-native engineering acceleration.
The important shift is organizational, not generative: AI makes writing code cheaper, so delivery quality increasingly depends on context, orchestration, validation, and governance.
- –Specialized agents divide the lifecycle into discrete responsibilities instead of relying on one general coding assistant
- –Human oversight remains central for architecture, compliance, security, and release decisions
- –The model exposes testing, observability, and requirements quality as the next major delivery bottlenecks
- –RayMish’s reported velocity and defect improvements are vendor claims, not independently verified benchmarks
- –This is best understood as a delivery framework and services proposition, not a standalone developer product
DISCOVERED
3h ago
2026-08-12
PUBLISHED
13h ago
2026-08-12
RELEVANCE
AUTHOR
shamshudein