How to incorporate AI into the SDLC
- Incorporating AI into the SDLC means applying AI tools across the phases of software delivery, from planning and development through testing, review, deployment, and operations.
- AI involvement spans a range: assistive tools suggest and summarize while humans execute, and agentic tools plan and carry out tasks themselves. The level of involvement determines the infrastructure and controls a team needs.
- A practical approach works phase by phase: adopt assistive tools where review discipline already covers the risk, and add isolated execution, preview-based verification, and controlled release before giving AI tools the ability to run code.
- For the execution-heavy levels of AI involvement, teams can run the full setup on Northflank, which provides microVM-backed sandboxes for running AI-generated code, Git-based builds, preview environments for verifying changes, staging and production environments with release workflows, and governance including RBAC, audit logs, and Bring Your Own Cloud. Get started (self-serve) or book a demo if you want a walkthrough.
AI tools now touch most phases of software delivery, but they touch each phase differently. This guide covers how to incorporate AI into the SDLC phase by phase, and what infrastructure each level of AI involvement requires.
Incorporating AI into the SDLC means applying AI tools within the phases of your existing software development lifecycle, such as code generation during development, test generation during QA, or diff summarization during review.
A useful way to plan adoption is by level of AI involvement:
- Assistive: the tool suggests and the human executes. Code completion, chat-based explanations, and diff summaries fall here. The developer runs everything, so existing review and CI discipline covers most of the risk.
- Agentic: the tool plans and executes. Coding agents can run commands, install dependencies, execute tests, and iterate on failures, which introduces execution risk that editor plugins do not have. We cover this level in depth in how to use agentic AI in your SDLC.
The level of involvement, not the phase, is what determines the infrastructure required. Assistive tools need adoption guidelines and review discipline. Agentic tools need isolated execution, verification against running systems, and a controlled release path. Teams that restructure their whole delivery process around the agentic level end up with what we define in what is the AI SDLC?.
Each phase below covers how AI tools are used today and what a team needs in place to adopt them safely.
| SDLC phase | Common AI uses | What teams need in place |
|---|---|---|
| Planning and requirements | Drafting specs, summarizing tickets, breaking down tasks | Human review of scope and acceptance criteria |
| Development | Code completion, code generation, refactoring, agentic coding | Review discipline; isolated execution for agents |
| Testing | Test generation, running suites, reproducing bugs | Runtime with real dependencies |
| Code review | Diff summaries, risk flagging, suggested fixes | A running preview alongside the diff |
| Deployment and release | Changes released through existing CI/CD and release workflows | Controlled environments, rollback |
| Operations | Log and metric investigation, incident summaries | Observability access with scoped permissions |
AI tools can draft specifications, summarize tickets and discussions, and break features into tasks.
This phase carries low execution risk, since nothing runs, so it is a common starting point. The main control is human review of scope and acceptance criteria, because generated specs can be plausible while missing constraints the team knows about.
Development is where AI involvement varies most, from code completion through chat-assisted coding to agents that write and test changes themselves.
Assistive tools fit into existing workflows with little infrastructure change. Agentic tools need an isolated place to run, since they execute commands and install dependencies and their output is untrusted until verified. MicroVM-backed sandboxes provide that isolation with container-like performance, and what is an AI sandbox? covers the concept in depth. Whichever level a team adopts, AI-generated code should reach the codebase through Git and pull requests so it stays traceable and reviewable.
AI tools can generate test cases, run suites, and help reproduce reported bugs.
Generated tests are only meaningful when they run against realistic dependencies, since a test that passes against mocks can still miss integration failures. That makes a runtime with real services, databases, and jobs part of the adoption requirement, not an optional extra.
AI can summarize diffs, flag risky patterns, and suggest fixes, which helps reviewers triage a higher volume of changes.
AI review assists human review rather than replacing it, and for AI-generated code the diff alone is rarely enough. A preview environment gives reviewers a running instance of the application for each pull request. On Northflank, a preview blueprint creates ephemeral environments for branches or pull requests, including the services, jobs, add-ons, and secret groups needed to test the change.
AI-originated changes should reach production through the same controlled path as human changes, not a separate or manual one.
On Northflank, staging and production are separate environments with release workflows that automate tasks such as backing up databases, running migrations, and deploying services. Workflow steps can be conditional, so a failed step stops the rest of the workflow, and teams can roll back to a previous deployment when needed. As AI raises the volume of changes, this controlled path is what keeps release quality steady.
AI tools can investigate logs and metrics, summarize incidents, and propose remediations.
Access should be scoped, since operational tooling touches production data. Role-based access control limits what tools and users can reach, and audit logs record changes to the platform. Observability itself, including logs and metrics, gives both humans and AI tools the signals they investigate.
The infrastructure requirement scales with the level of AI involvement rather than with the number of tools adopted.
Assistive adoption mostly needs policy: guidelines on where suggestions are acceptable, and review discipline that treats generated code like any other code.
Agentic adoption adds four platform requirements: isolated execution for untrusted code, builds from version control, verification against running environments, and a controlled, reversible release path. Teams with compliance or data residency requirements also need the option to run all of it inside their own cloud account, a model covered in why smart enterprises are insisting on BYOC for AI tools.
Northflank provides the execution, verification, release, and governance layers for the levels of AI involvement that run code.
| Requirement | Northflank capability |
|---|---|
| Isolated execution for AI-generated code | MicroVM-backed Sandboxes |
| Builds from version control | Git-based Dockerfile and buildpack builds |
| Verification against a running system | Preview environments via preview blueprints |
| Controlled promotion and rollback | Environments and release workflows |
| Access control and auditability | RBAC, audit logs, secret groups |
| Compliance and data residency | Northflank Cloud or Bring Your Own Cloud |
Teams can run agent sandboxes, application services, databases, and GPU workloads with the same deployment and environment model, and the documentation covers each layer.
You can get started (self-serve) with a free project, or book a demo if you would rather walk through your use case with the team first.
Low-execution-risk phases such as planning, code completion during development, and diff summarization during review are common starting points, since existing human review covers most of the risk. Phases where AI executes code need isolation and verification infrastructure in place first.
No. Incorporating AI into the SDLC means applying AI tools within your existing lifecycle, phase by phase. The AI SDLC describes a lifecycle restructured around agents as code producers, which is where teams often end up as AI involvement deepens.
Mostly no. Code completion and chat assistants run in the editor and the developer executes everything, so existing review and CI discipline covers most of the risk. Infrastructure requirements start when tools execute code themselves, which is the agentic level covered in how to use agentic AI in your SDLC.
Route it through Git and pull requests, verify it in a running preview environment rather than a diff alone, and release it through controlled environments with rollback. How to vibe code securely covers the security side in more detail.
Yes. With Bring Your Own Cloud, sandboxes, preview environments, and production workloads run inside your own AWS, GCP, or Azure account, which addresses data residency and compliance requirements.
- What is the AI SDLC?: the lifecycle for teams where agents generate a significant share of the code.
- How to use agentic AI in your SDLC: the adoption path for the agentic level of AI involvement.
- Agentic coding tools: a comparison of the coding agents teams are adopting.
- Top AI tools for CI/CD pipeline automation: where AI intersects with build and release automation.
- What CTOs should know about deploying AI-built apps: the leadership view on shipping AI-generated software.
