ENGINEERING · DELIVERY FRAMEWORK · V1.0.0

AI-assisted development lifecycle (AI DLC).

Show where AI accelerates software delivery and where independent engineering evidence remains essential.

REFERENCE FIGURE / NR-004CONCEPTUAL VIEW · NOT A DEPLOYMENT TOPOLOGY
AI-assisted development lifecycle (AI DLC). Show where AI accelerates software delivery and where independent engineering evidence remains essential.
nishantr · nishantr.techOpen full-size figure ↗
THE PROBLEM

Why this pattern exists

Fast code generation can increase changes faster than teams can validate behavior, design integrity, security and operations.

DESIGN GUIDANCE

Principles to preserve

  1. Human accountability and architectural decision ownership are explicit.
  2. AI can assist at every stage but does not remove verification gates.
  3. Test design and security controls should be created before or alongside implementation.
  4. Production signals feed requirements, evaluation and architecture change.
  5. Release promotion requires independently verifiable evidence.
THE REAL DESIGN WORK

Trade-offs, not just boxes

  • Stricter gates can slow local iteration while reducing production regressions.
  • Automated evaluations need maintenance and can drift as business behavior evolves.
  • Autonomous coding helps throughput only when test and deployment systems are reliable.
REVIEW QUESTIONS

Before you implement

  • What can the agent change and with what repository permissions?
  • Which tests are independent of generated implementation?
  • Who accepts an ADR or a business-rule exception?
  • What signals trigger rollback and learning?
MORE FROM THE COLLECTION
NR-002The reasoning–execution boundary
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