Pages on AIDLC.io as they are written and re-reviewed. https://aidlc.io/updates/ 2026-10-13T00:00:00Z RJ Lindelofhttps://aidlc.io/about/ https://aidlc.io/worked-example/ 2026-10-13T00:00:00Z

Follow one invented feature from a customer request to a production incident, with the actual artifact each AIDLC phase hands to the next one.

https://aidlc.io/agent-orchestration/ 2026-10-13T00:00:00Z

How to conduct a fleet of AI agents across the SDLC: a state machine that owns every transition, dual review, and fail-closed gates you can prove.

https://aidlc.io/tools/ 2026-10-13T00:00:00Z

A tool-agnostic map of the 2026 AI landscape - coding agents, MCP, orchestration, and observability - across all five phases of the AIDLC framework.

https://aidlc.io/code-review/ 2026-10-13T00:00:00Z

AI code review lifts measured quality while human oversight quietly erodes. See the 2025-2026 data, review standards, fatigue fixes, and merge ownership rules.

https://aidlc.io/governance/ 2026-10-13T00:00:00Z

Governance before rollout: five pillars, a policy outline, and vendor checklists for teams giving AI coding agents real access to code.

https://aidlc.io/security-and-compliance/ 2026-10-13T00:00:00Z

The AI SDLC threat model, data classification tiers, and how SOC 2, HIPAA, ISO 27001, NIST, and the EU AI Act map to it.

https://aidlc.io/templates/ 2026-10-13T00:00:00Z

Copy-paste templates for AI usage policy, ticket contracts, PR fields, reviewer checklists, agent hard rules, and vendor and incident checklists.

https://aidlc.io/phases/analyze/ 2026-10-13T00:00:00Z

Deep dive into the Analyze phase of AIDLC and see how AI assists with requirements gathering, research synthesis, and feasibility analysis.

https://aidlc.io/cheatsheet/ 2026-10-13T00:00:00Z

Quick reference cheatsheet for AIDLC methodology. Instant access to all five phases, key practices, tools, and implementation tips for AI-Native development.

https://aidlc.io/phases/curate/ 2026-10-13T00:00:00Z

Explore the Curate phase of AIDLC. Learn about AI-powered monitoring, intelligent debugging, continuous documentation, and iterative improvement.

https://aidlc.io/phases/develop/ 2026-10-13T00:00:00Z

Deep dive into the Develop phase of AIDLC. Learn how AI coding agents plan and execute multi-step tasks while you scope, steer, and review the diffs.

https://aidlc.io/glossary/ 2026-10-13T00:00:00Z

Plain-English definitions of the terms that matter in AI-native software delivery - agents, harnesses, context engineering, MCP, and spec-driven development.

https://aidlc.io/phases/ideate/ 2026-10-13T00:00:00Z

Master the Ideate phase of AIDLC. Explore AI-driven architecture design, documentation generation, and solution modeling before writing code.

https://aidlc.io/phases/launch/ 2026-10-13T00:00:00Z

Master the Launch phase of AIDLC. Learn about AI-generated tests, intelligent CI/CD configuration, deployment automation, and release validation.

https://aidlc.io/legacy-code/ 2026-10-13T00:00:00Z

How to use AI agents on legacy code safely: build context, pin behavior with characterization tests, strangle in slices, and upgrade in small batches.

https://aidlc.io/prompts/ 2026-10-13T00:00:00Z

A tool-agnostic library of prompts for every AIDLC phase - copy, adapt, and use them to scope work, steer agents, and review what comes back.

https://aidlc.io/aidlc-vs-ai-dlc/ 2026-10-13T00:00:00Z

AIDLC and AWS AI-DLC share a name but not a shape. Compare the five-phase methodology with AWS's 33-stage workflow engine and see when to use each.

https://aidlc.io/aidlc-vs-ml-lifecycle/ 2026-10-13T00:00:00Z

AI development life cycle means two things: building software with AI agents (AIDLC) or building a model (MLOps). See the table, standards, and where they meet.

https://aidlc.io/scrum-and-kanban/ 2026-10-13T00:00:00Z

Run AIDLC inside Scrum or Kanban: map the five phases to sprint events, size work by review effort, cap review WIP, and define Done for agent-authored code.

https://aidlc.io/privacy/ 2026-10-13T00:00:00Z

AIDLC.io collects anonymous usage statistics and what you send through the contact form. What is collected, why, for how long, and how to opt out.

https://aidlc.io/context-engineering/ 2026-10-13T00:00:00Z

Context engineering and spec-driven development are the core skill of AI-native delivery: how to write specs, steer agents, and review their output well.

https://aidlc.io/engineering-practices/ 2026-10-13T00:00:00Z

Eleven engineering practices for AI-assisted delivery: context files, ticket contracts, plan-first review, mutation testing, and risk-tiered review.

https://aidlc.io/field-notes/ 2026-10-13T00:00:00Z

First-hand notes from two production AI systems: the language drift that cost weeks, why a non-frontier model won, and the habit that changed everything.

https://aidlc.io/choosing-a-coding-agent/ 2026-10-13T00:00:00Z

A framework for choosing an AI coding agent - the axes that actually matter, how to run a fair trial on real work, and the mistakes to avoid.

https://aidlc.io/learn/ 2026-10-13T00:00:00Z

Master the AIDLC framework with comprehensive training materials, examples, and best practices for implementing AI-Native Software Delivery.

https://aidlc.io/measuring-impact/ 2026-10-13T00:00:00Z

Which pre-AI baseline to capture, which metrics to report, which vanity numbers to skip, and the quarterly test that flags a queue, not a gain.

https://aidlc.io/agents-in-the-pipeline/ 2026-10-13T00:00:00Z

How to run coding agents in CI safely: service identity, scoped permissions, sandboxing, budgets, and seven hard rules enforced in the workflow.

https://aidlc.io/teams-and-skills/ 2026-10-13T00:00:00Z

How roles shift when agents write first drafts: junior skill formation, interviewing, upskilling, review fatigue, and measuring developer experience.

https://aidlc.io/cost-model/ 2026-10-13T00:00:00Z

How seats, seat allowances, and API usage make up AI engineering spend, plus the four-position build-vs-buy framework for agent tooling.

https://aidlc.io/productivity-paradox/ 2026-10-13T00:00:00Z

Faros AI, DORA, and METR data on the AI productivity paradox: individual output rises while organizational throughput and stability often do not.

https://aidlc.io/maturity-model/ 2026-10-13T00:00:00Z

Score your team against five AI SDLC maturity levels, from no AI to governed and optimized, with a 12-question interactive self-assessment.

https://aidlc.io/playbook/ 2026-10-13T00:00:00Z

The hub for AI in the software development lifecycle: ten operating rules, three eras of AI, and where each phase gets help or fails.

https://aidlc.io/research/ 2026-10-13T00:00:00Z

Every study, survey, and standard the AI SDLC Playbook cites, with the key finding from each and a link to the primary source.

https://aidlc.io/rollout-playbook/ 2026-10-13T00:00:00Z

A four-phase timeline for rolling out AI-assisted engineering: baseline in three weeks, assist, delegate, and compound - with exit criteria for each phase.

https://aidlc.io/ai-native-sdlc/ 2026-10-13T00:00:00Z

The seven pillars of an AI-native SDLC: context and measurement as platform investments, plus five workflow shifts from planning to maintenance.

https://aidlc.io/hidden-risks/ 2026-10-13T00:00:00Z

The 13-risk register for AI-assisted development, backed by CodeRabbit, GitClear, Veracode, Apiiro, and developer trust survey data.

https://aidlc.io/state-of-ai/ 2026-10-13T00:00:00Z

An autumn 2026 snapshot of AI development: agents as the default, portable agent standards, and the discipline of verifying work you did not write.

https://aidlc.io/failure-patterns/ 2026-10-13T00:00:00Z

The same twelve failure patterns recur across AI adoptions of every size - each one has a name, an observable tell, and a documented fix.