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AI development lifecycle

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 records how each change was created, tested, approved and released.

Book an AI-DLC workshopSee the lifecycle
moring AI-DLC  /  change recordIllustrative
Change
svc-payments / PR 4182In review
Recorded for this change
Identity and permissionsVerified
Approved models and context3 of 11
Tool callsLogged
Tests and evaluationsPassed
Reviewer
Security engineering
Rollback
Defined
Runs where you already run
Oracle Cloud InfrastructureMicrosoft AzureAmazon Web ServicesKubernetesGoogle CloudRed Hat OpenShift
Why AI-DLC

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.

The AI development lifecycle

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.

Change request
Task Add retry to payout webhookDefined
Owner Payments engineeringAssigned
Boundary No schema changesSet
Acceptance 3 criteriaAgreed
Stage 01

Define the change

Set the task, owner, boundaries and expected outcome.

What is recorded
Request, owner and acceptance criteria.
Approved resources
Repositories2 of 14
Models3 of 11
Documentation5 sources
Tools MCP servers4 allowed
Stage 02

Control access and context

Set which repositories, models, documentation and tools may be used.

What is recorded
Identity, permissions and approved resources.
Activity log
Context 12 files readLogged
Tool call run_testsLogged
Tool call write_file ×4Logged
Tool call deploy_stageBlocked
Stage 03

Build with approved tools

The developer or engineering agent completes the work within those controls.

What is recorded
Model, context, tool calls and resulting change.
Checks
Unit and integration142 passed
Code qualityPassed
Security scan1 finding
Policy compliancePassed
Stage 04

Evaluate the output

Test the change for functionality, quality, security and policy compliance.

What is recorded
Tests, evaluation results and failed checks.
Review and release
Engineering reviewApproved
Security reviewApproved
Release canary 10%Live
Rollback on error rate >1%Armed
Stage 05

Review and release

Route the change to the required engineering or security reviewer.

What is recorded
Approval, release and rollback conditions.
Outcomes · last 30 days
Usage1.2k
Cost$3.1k
Review time−29%
Defects−18%
Releases41
Stage 06

Measure and improve

Track outcomes and re-evaluate when the workflow changes.

What is recorded
Usage, cost, defects and release outcomes.
Controls and change history

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.

01

Who made the change

Developer or engineering agent, team and repository.

02

What the AI used

Context, models, MCP servers and engineering tools.

03

What was checked

Tests, evaluations, security and policy conditions.

04

What happened next

Review, approval, release, rollback and operating outcome.

The result is one reviewable record from request to release.

How moring delivers AI-DLC

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.

See how moring engineers build and deploy workflow agents →

Coding assistants and engineering agents
AI-DLC
Identity
Repositories
Models and context
Release process
AI-DLC and the AI Control Plane

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.

Runtime

AI Control Plane

Controls what agents may access, do and change as they operate.

Explore the AI Control Plane →

Delivery

AI-DLC

Controls how AI-assisted software changes are built, evaluated and released.

See the lifecycle →

Engineering outcomes

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-DLC workshop

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 workshop
What the session covers
How AI enters your development process today
Access, context and approved tools per team
Checks required before a change is released
The change record your reviewers need

Frequently asked questions

What is AI-DLC?+
A governed lifecycle for AI-assisted software delivery. It connects coding assistants and engineering agents to approved context, models and tools, then records how each change is evaluated, approved and released.
How is AI-DLC different from the SDLC?+
The SDLC remains the core process. AI-DLC adds the controls and evidence required when coding assistants and engineering agents participate in it.
Does AI-DLC replace existing coding assistants or developer tools?+
No. AI-DLC governs how approved assistants, models, tools and context are used across your existing development process.
How does AI-DLC work with the AI Control Plane?+
The AI Control Plane provides shared controls for identity, access, tools, approvals and runtime behavior. AI-DLC applies those controls across code changes, evaluations, reviews and releases.
What do moring engineers implement?+
They map the current delivery process and connect AI-DLC to identity, repositories, approved models, context, tools, evaluations and release systems.
What should be evaluated before release?+
Before release, teams evaluate the engineering task, code quality, tests, security, policy compliance, tool access, failure handling and rollback conditions.
Book the workshop

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.

A map of how AI is used across your delivery process today
The control gaps your reviewers will raise before release
An implementation sequence for closing them
Who to bring: engineering leads, security, and release owners

Book an AI-DLC workshop

One session, your repositories, your release process.