Best practices for shipping software safely using AI

AI can make software teams feel faster before the release system is actually safer. A developer can generate an implementation, a test file, a migration, a pull request description, and release notes in one sitting. That is useful. It also means the old bottlenecks move downstream: review, QA, security, observability, rollout ownership, and rollback planning all get compressed into a smaller window.

The best practices for shipping software safely using AI are not about slowing the team back down. They are about making the release path explicit enough that faster coding does not turn into uncontrolled production exposure.

For product and engineering teams, the core question is simple: if AI helps create the change, what keeps the release accountable? The answer is a stack of ordinary software delivery practices applied with more discipline: scoped tasks, human review, feature flags, staged rollouts, evals, guardrail metrics, and cleanup criteria.

Treat AI as a release accelerator, not a release owner

The most dangerous AI release workflow is the one that lets code generation blur ownership. AI can draft, refactor, summarize, test, and inspect. It cannot own the business context, the user risk, or the production decision.

That distinction should be visible in the workflow. Before an AI-assisted change moves toward production, the team should know who owns the requirement, who owns the code, who approves exposure, and who will respond if a rollout goes wrong. If those responsibilities are unclear, the team is not ready to ship even if the implementation compiles.

This matches the broader direction in NIST's secure software development practices for generative AI, which extends secure development practices to AI systems rather than treating AI as a separate universe. AI-assisted software still needs secure design, review, testing, provenance, and operational controls.

Community discussions reach the same point from the other side. Engineers on Hacker News and Reddit often disagree about how much AI speeds coding itself, but the practical concern is consistent: faster code generation does not automatically remove requirements, review, QA, or deployment work.

The release policy should make that explicit:

ai_assisted_release:
  owner: engineering
  required_checks:
    - human_code_review
    - security_review_for_sensitive_paths
    - flag_off_test
    - flag_on_test
    - rollout_guardrails
  default_exposure: internal_only
  rollback: feature_flag_off

That kind of small contract is not bureaucracy. It is how the team keeps AI in the role where it is strongest: accelerating work that humans still understand and own.

Scope the task before the agent writes code

AI tools perform better when the task has clear boundaries. "Build the new onboarding flow" is not a release-safe prompt. "Add the new onboarding checklist behind the onboarding-checklist-v2 flag, preserve the current path as fallback, instrument completion events, and add tests for flag-off and flag-on states" gives the agent a release-aware shape.

The difference is not just prompt quality. It changes what reviewers get back. A scoped AI task can be inspected against a checklist. An open-ended AI task often creates hidden decisions that reviewers have to discover after the fact.

Good AI release tasks define:

• The user or account segment affected.
• The GrowthBook flag or experiment that will control exposure.
• The fallback behavior when the flag is off.
• The files or modules the agent should touch.
• The events, logs, or metrics that should prove the change is working.
• The tests that must pass before the pull request is considered reviewable.

This is where GrowthBook's AI-native development platform matters. The useful idea is not "let an agent ship for you." It is "bring the agent into the same release controls the team already uses." If the AI tool can create or inspect flags, experiment specs, and rollout rules, it can work against real release objects instead of inventing an implementation in isolation.

For teams using the GrowthBook MCP server, that means an agent can help find or create the flag that controls a change, then write code against that known object. The human reviewer still owns the decision to merge, expose, and expand.

Put AI-assisted changes behind reversible controls

AI increases the number of plausible changes a team can make. That makes reversible release controls more important, not less.

Feature flags are the practical foundation because they separate deployment from exposure. GrowthBook's feature flag docs describe the core pattern: control application behavior without deploying new code, target users, roll out gradually, or run experiments. For AI-assisted software delivery, this turns the release from a binary deploy into a controlled exposure sequence.

The Google SRE workbook on canarying releases frames the release problem as exposing a change to a small portion of real traffic before broad rollout. That idea becomes more valuable when the implementation was AI-assisted because the team may have moved faster than its normal manual discovery process.

There are three useful levels of control:

GrowthBook feature rules support forced values, percentage rollouts, experiments, and Safe Rollouts. The important release habit is deciding which rule type matches the risk. Internal testing may use a forced value. A low-risk UI improvement may use a manual percentage rollout. A user-facing workflow change may need a Safe Rollout or an experiment.

For teams that want automated monitoring, Safe Rollouts release a feature to a subset of users while watching guardrail metrics for regressions. That is a better default than hoping someone notices a dashboard after the rollout has already reached most users.

Review AI-assisted code for the risks AI is likely to miss

AI-generated code can be syntactically polished while still missing the thing that matters. It may skip authorization checks, choose an unsafe dependency, misunderstand a business rule, under-test the fallback path, or add a clever abstraction that nobody wants to maintain.

The review process should be specific to those failure modes. A generic code review that asks "does this look right?" is too weak for an AI-assisted release.

At minimum, reviewers should check:

• Does the change match the requirement, or did the agent solve a neighboring problem?
• Is the fallback behavior still working and tested?
• Are all sensitive paths covered by authentication, authorization, input validation, and logging?
• Did the agent add or update dependencies, generated files, migrations, or background jobs?
• Are error states observable, or will failures disappear into logs nobody reads?
• Does the code keep the feature flag boundary clean enough to remove later?
• Did the tests cover both flag states and the most likely user edge cases?

Security deserves its own pass. OWASP's Top 10 for Large Language Model Applications and AI Agent Security Cheat Sheet are useful reminders that AI features and AI-assisted workflows introduce risks around prompt injection, tool permissions, data leakage, and over-trusted automation. Those risks do not replace normal application security; they sit on top of it.

This is also where AI can help the reviewer. Ask the agent to produce a release-risk review after implementation, but treat the output as a checklist, not a verdict. A useful prompt is:

Review this AI-assisted change for release risk.
Focus on flag boundaries, fallback behavior, tests, authorization, observability,
dependencies, data access, and cleanup work.
Return only concrete issues and the files or functions where they appear.

The reviewer still decides what is real. The agent helps search the space.

Measure release safety and user value separately

One of the easiest mistakes in AI-assisted delivery is treating "the release did not break" as proof that the change helped. Those are different questions.

Release safety asks whether the change can expand without hurting system health or user trust. Product value asks whether the change improves a user outcome. Teams need both.

Guardrail metrics should catch harm early:

• Error rate and exception volume.
• p95 or p99 latency.
• Support contact rate.
• Checkout, signup, or activation regressions.
• API cost or infrastructure cost.
• Rollback frequency.
• Segment-level issues hidden by aggregate metrics.

Outcome metrics should prove value:

• Task completion.
• Activation.
• Retention.
• Conversion.
• Expansion.
• Revenue impact.
• Time saved in the workflow.

If the change affects AI behavior directly, add quality measures too. The OpenAI evaluation best practices guide is useful because it emphasizes task-specific eval design. Do not measure an AI support assistant with a generic benchmark if the real failure mode is whether users get a useful answer without opening a ticket.

GrowthBook feature flag experiments connect the release control to the measurement layer. A feature can start as a guarded rollout and become an experiment when the team needs evidence that the new behavior is better, not merely safe enough to expose.

Keep release artifacts inspectable

AI-assisted releases should leave a trail that another human can understand. That trail does not need to be heavy, but it should answer the questions people ask during incidents and post-launch reviews.

For each meaningful AI-assisted release, keep:

• The feature flag key.
• The rollout or experiment rule.
• The owner.
• The fallback behavior.
• The primary metric and guardrails.
• The human reviewer.
• The cleanup condition.
• The decision record after rollout or experiment analysis.

This protects the team from two common failure modes. The first is the mystery flag: a conditional path nobody wants to remove because nobody remembers why it exists. The second is the mystery win: a change that shipped, looked fine, and became permanent without anyone documenting what actually improved.

The OpenFeature evaluation context specification is helpful here because it separates the evaluated user or request context from the flag decision. In practice, that means teams should log enough context to know which users were exposed, which variant they received, and which release rule produced the result.

The practitioner warning is familiar: feature flags reduce release risk, but unmanaged flags become debt. Discussions on Hacker News and Reddit repeatedly come back to cleanup and test coverage. AI-assisted shipping makes that more urgent because it can create more flagged paths faster than the team is used to retiring them.

Build the minimum safe AI release loop

The safe starting point is smaller than most teams think. You do not need a perfect AI governance program before shipping one AI-assisted change. You do need a loop that keeps speed connected to control.

A minimum safe loop looks like this:

1. Define the task and the release owner.
2. Create or identify the GrowthBook flag before implementation.
3. Ask the agent to implement against that flag and fallback.
4. Review the code, tests, security, and observability.
5. Deploy with the feature off by default.
6. Expose internally first.
7. Expand with a percentage rollout, Safe Rollout, or experiment.
8. Decide based on guardrails and outcome metrics.
9. Remove stale flags or losing variants after the decision.

The important part is sequencing. Do not generate the code first and decide how to release it later. That is how teams end up with a fast implementation and a risky launch. Put the release path into the task before the agent starts.

GrowthBook is useful here because feature flags, experimentation, targeting rules, Safe Rollouts, and agent workflows can sit in the same operating model. The agent can help create and inspect release objects, but the team still owns the exposure decision.

What to do next

Pick one upcoming AI-assisted change and run it through the full loop. It should be real enough to matter, but not so high-risk that the first test becomes a governance project.

Good candidates include:

• A new AI-generated summary in an internal workflow.
• A recommendation or ranking tweak behind an existing flag.
• A support or onboarding helper with a deterministic fallback.
• A refactor generated by AI where the visible behavior should not change.
• A narrow UI workflow that can be exposed to employees first.

The point is to practice the release system, not to prove that AI can write code. AI can already help teams write more code. The advantage goes to teams that can review, expose, measure, and roll back that code without losing control of the product.

Safe AI-assisted shipping is not a separate process. It is disciplined software delivery with a faster implementation engine plugged into it. Keep the human owner, keep the flag boundary, keep the metrics, and keep the rollback path close. That is how AI becomes a release advantage instead of a production liability.