27 docs tagged with "aidlc"

AI Coding Agents for AIDLC Construction Phase — Kiro Spec-Driven Development, Q Developer, Agent Comparison

Core philosophy of AIDLC and the Intent → Unit → Bolt execution model

AIDLC official Adaptive Workflows — conditional stage execution decision tree, Inception 7-stage and Construction per-unit loop explained

Illustrative financial, manufacturing, public-sector, and fintech scenarios for planning AIDLC adoption and harness validation

AWS Labs AIDLC official 11 common rules explained — Question Format through Audit Logging with enterprise adoption guide

AIDLC Enterprise Adoption — Organizational transformation, cost estimation, governance, and case studies

AIDLC Checkpoint Approval gates and ISO 8601-based audit logs — Implementation guide for AIDLC audit trails in regulated industries

AI Agent-based autonomous incident response — Strands/Kagent integration, Chaos Engineering + AI, ontology feedback loop

Quantifying AIDLC Cost Effectiveness — RFP Estimation Model, Ontology/Harness ROI, Open Weight TCO Comparison

Declarative automation patterns for implementing AIDLC Construction/Operations with EKS Capabilities

AIDLC Enterprise Adoption Strategy — Waterfall→Hybrid Transition, Champion Model, Phased Rollout Roadmap

AWS Labs AIDLC Extension System — integrate organization-specific security, compliance, and domain rules into AIDLC workflows via opt-in mechanism

AIDLC Enterprise Governance — 3-Layer Model, Steering File Automation, Data Sovereignty, AI Act Compliance

Required harnesses and implementation guide by MSA pattern

The second axis of AIDLC reliability — Harness design that architecturally enforces AI execution safety

Application guide for Level 1 simple CRUD services and Level 2 synchronous MSA orchestration patterns

Application guide for Level 3 async event-driven MSA and Level 4 Saga + compensating transaction patterns

Application guide for Level 5 distributed transactions + CQRS + Event Sourcing patterns

Diagnose MSA difficulty as Level 1-5 in enterprise environments and provide integrated pattern-specific guides, harnesses, and verification

The data foundation of AIDLC Operations — building 3-Pillar observability + AI analysis layer

The first axis of AIDLC reliability — An ontology approach to prevent AI hallucination and ensure domain accuracy through a Typed World Model

Ontology depth and writing guidelines by MSA complexity level

Open-weight model utilization strategies for data residency and cost optimization — on-premises deployment, hybrid configuration, TCO comparison

ML-based predictive scaling and anomaly detection — Karpenter+AI, CloudWatch Anomaly Detection, AI Right-Sizing

Team Structure and Role Changes in the AIDLC Era — Harness Engineer, Ontology Steward, AI Verifier

AIDLC technology investment decision-making — Build-vs-Wait matrix, tool maturity assessment, 6/12/18-month horizons

Verification methods to ensure quality when applying AIDLC in complex MSA