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AIDLC: AI-Driven Development Lifecycle

Reading time: ~3 minutes

AIDLC (AI-Driven Development Lifecycle) is a new development methodology where AI drives the entire software development process. While traditional SDLC (Software Development Lifecycle) was a human-centric process, AIDLC accelerates the entire development cycle through the Intent → Unit → Bolt model, where AI leads from requirements analysis to design, implementation, and testing.

Core Concepts​

AIDLC is built on three pillars:

  • Intent: Humans define requirements and business intent in natural language. Kiro's Spec-driven development (requirements → design → tasks → code) supports this stage.
  • Unit: AI decomposes intent into actionable unit tasks. Quality is ensured by combining DDD (Domain-Driven Design) with BDD/TDD.
  • Bolt: AI automatically executes code generation, test writing, and deployment pipeline configuration.

Reliability Dual Axis: Ontology × Harness​

To systematically ensure the reliability of AI-generated code, AIDLC introduces a two-axis reliability framework:

  • Ontology (WHAT + WHEN): A typed world model formalizing domain knowledge. A living model that continuously evolves through its own feedback loops (Inner/Middle/Outer), preventing AI hallucinations.
  • Harness Engineering (HOW): Architectural structure for validating and enforcing constraints defined by the ontology

10 AIDLC Principles​

The AIDLC framework defines 10 principles for systematizing AI-driven development. See AIDLC Framework for details.

After Development: Operations and Feedback Loops​

After developing software with AIDLC, continuous improvement and feedback loops in the production environment are essential. See AIOps for an approach to this. AIOps is a methodology for systematically building feedback loops for operational efficiency including observability, predictive scaling, and auto-remediation using AI.

Learning Path
  1. AIDLC Framework — 10 principles, Intent→Unit→Bolt model, DDD integration, EKS capabilities mapping
  2. AIOps — Building operational feedback loops after development

References​