How to use agentic AI in your SDLC

Agentic AI changes how work moves through the software development lifecycle, because agents act rather than only suggest. This guide covers where agentic AI fits in the SDLC and the practical steps for adopting it without lowering your quality bar.

Agentic AI in the SDLC means using AI agents that can plan and carry out engineering tasks across the development lifecycle, from writing and testing code to preparing changes for release.

The distinction from earlier AI coding tools is execution. Code completion suggests lines in an editor. An agent can clone a repository, install dependencies, run a development server, execute tests, read the failures, and iterate until the task is done. Tools in this category include coding agents such as Claude Code, Cursor agents, and similar systems, covered in more depth in agentic coding tools.

That execution capability is what makes adoption an infrastructure question. An assistant that only suggests code needs an editor plugin. An agent that runs code needs a place to run it, a way to verify its output, and a controlled path to production. When teams restructure their whole delivery process around this, the result is what we define in what is the AI SDLC?, the lifecycle for teams where agents generate a significant share of the code.

Agents can contribute at most stages of the lifecycle, but each stage has different requirements for running them safely.

The pattern across the table: the further an agent's work moves toward production, the more the surrounding controls come from the platform rather than the tool. Choosing an agent is the smaller decision. Deciding where it runs and how its output is verified is the larger one.

The steps below give agents room to work while keeping verification and release under team control.

Agents perform best on tasks with clear success criteria, such as fixing a failing test, adding an endpoint to an existing pattern, writing test coverage, or upgrading a dependency.

Starting narrow gives the team a baseline for how much agent output survives review, which informs how far to expand. Broad, open-ended tasks can produce large changes that are harder to review and verify, so they are a poor starting point for most teams.

An agent that executes commands and installs dependencies should not do so on a developer laptop, a shared CI runner, or anything with access to production systems.

Its output is untrusted until verified, so the execution environment should be isolated. Northflank Sandboxes provide microVM-backed containers with VM-level isolation and container-like performance, where an agent can clone the repository, install dependencies, run tests, and expose services. Developers can open a shell into the running sandbox or connect over SSH where enabled. The sandboxes documentation covers setup, and how to sandbox AI agents explains the isolation options in more depth.

Agent-generated changes should enter the codebase the same way human changes do: committed to a branch, pushed to Git, and opened as a pull request.

This keeps AI-generated code traceable, including what changed, which commit was built, and which release reached production. It also keeps humans in the loop at the point of approval. On Northflank, a pushed commit can trigger builds directly from the Git repository, so the same commit the agent produced becomes the artifact that moves through the rest of the lifecycle.

Agent-generated code can look correct in a diff and still fail when the service starts, the migration runs, or the frontend calls the backend, so review should include the running system.

A preview environment turns each pull request into a running, full-stack instance of the application. On Northflank, teams define a preview blueprint that creates ephemeral environments for Git branches or pull requests, including the services, jobs, add-ons, and secret groups needed to test the change. Reviewers can use the feature, QA can test it, and engineers can inspect logs and debug the running containers. As agents raise the volume of changes, this stage carries more of the verification load than static review can.

Approved changes should move to production through the same controlled path as any other change, typically staging first.

On Northflank, staging and production are separate environments, each with its own services, jobs, add-ons, and release workflows. Workflows automate release tasks such as backing up databases, triggering builds, running migrations, and deploying services. Steps can be conditional, so if a step fails the rest of the workflow does not continue, and teams can roll back to a previous deployment when needed. This gives agent-originated changes a controlled and reversible release path rather than a manual one.

Agentic adoption raises questions from security and platform teams that the process should answer up front.

Useful controls include role-based access control to scope what agents and developers can touch, secret groups so credentials are injected rather than embedded, and audit logs to record who or what changed the platform. Teams with data residency or compliance requirements can run the whole setup inside their own cloud account with Bring Your Own Cloud. Enterprise AI coding agent deployment covers these requirements in more detail.

Northflank provides the execution, verification, release, and governance layers that agentic AI adoption depends on, in one platform.

Teams can run agent sandboxes, application services, databases, and GPU workloads with the same deployment and environment model, on Northflank's managed cloud or inside their own cloud account. The documentation covers each layer.

AI coding assistants suggest code inside an editor, and the developer executes everything. Agentic AI plans and carries out tasks, including running commands, executing tests, and iterating on failures, which is why it needs an isolated execution environment.

They are related but not identical. Using agentic AI in the SDLC means applying agents within your existing lifecycle, while the AI SDLC describes a lifecycle restructured around agents as code producers, with isolation, preview-based verification, and release controls built in.

Agents that execute commands or install dependencies produce untrusted output, so they benefit from strong isolation. MicroVM-backed sandboxes provide VM-level isolation with container-like startup and performance. See best sandboxes for coding agents for a comparison.

Development tasks with clear success criteria, such as fixing failing tests or adding endpoints to existing patterns, are a common starting point. They produce reviewable changes and give the team a baseline before expanding agent use to other stages.

Yes. With Bring Your Own Cloud, sandboxes, previews, and production workloads run inside your own AWS, GCP, or Azure account. Why smart enterprises are insisting on BYOC for AI tools explains the drivers behind this model.

• What is the AI SDLC?: the lifecycle for teams where agents generate a significant share of the code.
• Agentic coding tools: a comparison of the coding agents teams are adopting.
• Agent-native software development: what it takes to deploy the software coding agents build.
• How to sandbox AI agents: isolation options for agent execution.
• Top AI tools for CI/CD pipeline automation: where AI intersects with build and release automation.