AI-DLC: build, evaluate and release AI-assisted software with control
Moring engineers connect coding assistants and engineering agents to approved context, models, and tools. AI-DLC, the AI Development Life Cycle, brings AI-driven development under control: it records how each change was created, tested, approved and released.
AI adoption is moving faster than engineering controls
Coding assistants and engineering agents enter organizations team by team. Different groups use different models, context, extensions and tools.
AI-DLC extends the existing software development lifecycle so teams can control how AI participates in a change and review what happened before and after release.
Access differs across teams
Coding assistants and engineering agents reach repositories, context, models and tools through different permissions.
AI activity is disconnected
Prompts, context and tool calls are rarely connected to the resulting pull request and release.
Outcomes are hard to measure
Tool adoption is visible. Its effect on delivery, quality, cost and risk is harder to establish.
AI-driven development needs a lifecycle, not a policy
AI-driven development means the work of building software is done in a session with a coding assistant or an engineering agent, rather than typed line by line. The output can be excellent and still be unreviewable, because the session leaves no record of which context the model saw, which tools it called, or which person accepted the result. A policy tells people what not to paste. A lifecycle decides what the system will accept as a change.
AI-driven development
Writing software through a model conversation: the developer or agent proposes, the environment evaluates, and a person accepts. The speed comes from the loop, and so does the exposure.
AI Development Life Cycle
AI-DLC is the governed version of that loop. Every stage from requirement to audit trail gets a named owner, defined boundaries and a structured record, so a change can be reconstructed after the fact.
AI-SDLC
AI-SDLC is the umbrella term for any software lifecycle that has AI tools in it. It describes that AI is present. It does not by itself say who approved a change or which evidence supports it.
Manage every change from intent to measured outcome
Set the task, owner, boundaries and expected outcome.
Set which repositories, models, documentation and tools may be used.
The developer or engineering agent completes the work within those controls.
Test the change for functionality, quality, security and policy compliance.
Route the change to the required engineering or security reviewer.
Track outcomes and re-evaluate when the workflow changes.
Define the change
Set the task, owner, boundaries and expected outcome.
Control access and context
Set which repositories, models, documentation and tools may be used.
Build with approved tools
The developer or engineering agent completes the work within those controls.
Evaluate the output
Test the change for functionality, quality, security and policy compliance.
Review and release
Route the change to the required engineering or security reviewer.
Measure and improve
Track outcomes and re-evaluate when the workflow changes.
See how AI contributed to every software change
AI-DLC connects the person or agent doing the work to the context, models, tools, checks and approvals behind the resulting change.
Who made the change
Developer or engineering agent, team and repository.
What the AI used
Context, models, MCP servers and engineering tools.
What was checked
Tests, evaluations, security and policy conditions.
What happened next
Review, approval, release, rollback and operating outcome.
The result is one reviewable record from request to release.
Moring engineers build the governed lifecycle with your team
Moring engineers map how AI enters your development process and connect AI-DLC to your identity, repositories, approved models, context, tools, evaluations and release systems.
Keep the coding assistants and developer tools your teams already use. Govern how they participate in software delivery.
Control how agents operate and how software changes
The AI Control Plane governs agent identity, access, approvals and runtime behavior. AI-DLC applies those controls across code changes, evaluations, reviews and releases.
AI Control Plane
Controls what agents may access, do and change as they operate.
AI-DLC
Controls how AI-assisted software changes are built, evaluated and released.
Measure more than tool adoption
Connect assistant and agent usage to the engineering outcomes your teams already review.
- 01Usage and cost by team, repository and workflow
- 02Pull-request and review time
- 03Tests, defects and rework
- 04Security and policy findings
- 05Releases, incidents and rollbacks
AI agent cost observability: spend you can attribute and defend
With agents writing code, running tests and opening pull requests, engineering spend stops being a licence line and becomes per-agent, per-workflow model and token cost. AI-DLC treats cost observability as a first-class component of the lifecycle, not a finance report after the fact.
- 01Attribution: every model and tool call carries the agent, workflow and cost centre that caused it
- 02Budgets and limits: spend and step ceilings per agent stop runaway loops before the invoice does
- 03Shared-cost allocation: tracing platform spend to the bill or staff-time estimate behind it, without double counting
- 04Value pairing: cost read against turnaround and quality for the same cases, so decisions hold up in review
Close the control gaps in AI-assisted software delivery
Map how coding assistants and engineering agents access context, use tools, complete checks and move changes toward release.
Book an AI-DLC workshopEngineering guides for the AI Development Life Cycle
Explore the difference between AI-DLC and standard SDLC, software delivery workflows, and how to govern AI coding agents.
AI-DLC vs. AI-SDLC: What's the Difference?
Why bolt-on SDLC checks fail for autonomous coding agents and what an evidence-first lifecycle requires.
Read comparison guide →AI-DLC Software Delivery Workflow
Step-by-step pipeline for connecting engineering assistants to approved context, model policies, and git commits.
Read workflow guide →Governing AI Coding Agents & Production Codebases
How to enforce architectural guidelines, security linters, and evidence records before AI code merges.
Read governance guide →AI Coding Agent Governance: What to Control Before Agents Write Production Code
One identity per agent, scoped repository access, security checks on every pull request and a human merge.
Read coding agent governance →Vibe Coding Enterprise Risk: The Problem Is Not That AI Writes Code
Why a ban fails, and what a tested, recorded path for AI-assisted coding looks like instead.
Read the risk analysis →What Should You Budget to Get AI Agents Into Production?
Per-agent cost attribution, spend ceilings and the AI agent cost observability a finance team will ask for.
Read the cost breakdown →Enterprise AI Agent Governance Framework
Understand how runtime policies, identity boundaries, and human sign-offs secure autonomous systems.
Explore AI Governance Hub →Frequently asked questions
What is AI-DLC?+
How is AI-DLC different from the SDLC?+
Does AI-DLC replace existing coding assistants or developer tools?+
How does AI-DLC work with the AI Control Plane?+
What do Moring engineers implement?+
What is AI-driven development?+
What should be evaluated before release?+
Start with a session on your own delivery process.
Tell us how AI enters your development process today and which checks your reviewers require. We reply within one business day.
Book an AI-DLC workshop
One session, your repositories, your release process.