Regulated AI software delivery

Let teams use AI coding tools in regulated software delivery while preserving scope control, approval evidence, and audit-ready records.

Regulated delivery

Give AI-assisted changes the same control discipline as production work

Regulated software teams can use AI coding tools when the delivery path still proves scope, policy, approvals, checks, and artifact identity. Hakama puts that evidence layer around the repo so AI-assisted work enters the process through a governed control point.

Policy: Rules apply before acceptance
Evidence: Receipts describe the run
Commit: Output ties back to the control path

Control baseline

Controls for regulated AI delivery

The baseline is simple: define what the assistant may do, check the result, require sign-off for risk, and keep the proof.

Declared scope

The task defines files, dependencies, routes, schema, and systems the work may touch.

Protected boundaries

Sensitive paths and policy exceptions require checks or approval before the work moves forward.

Required evidence

Claims about tests, scope, and policy are backed by execution output and receipts.

Commit enforcement

The repo accepts the change only after the governed run clears the configured rules.

Audit-ready proof

Evidence regulators and internal reviewers can inspect

Which policy applied?

The configured rule set for the governed workflow.

Which risk required sign-off?

Protected path, exception, or change type that triggered approval.

Which checks supported acceptance?

Execution output and pass or fail state.

Which change reached delivery?

Commit identity tied to the governed run.

How it runs

Govern the AI path before CI sees the result

Launch AI work under policy

hakama watch launch claude

Engineers keep their assistant, while the session runs with scope and risk controls active.

Validate the finished work

hakama exec

Hakama checks the diff against the accepted scope, approvals, and required evidence.

Hand CI a controlled change

CI remains valuable; it receives a change that already passed the governance checks around AI-assisted work.

Delivery architecture

Governance belongs close to the repo

Regulated teams need evidence at the point where code changes. Hakama runs locally around the repo and AI session, keeping policy and receipts close to the files, commands, and commits they describe.

Scope lives beside the working tree.
Approval gates trigger during the run.
Receipts attach to the delivery artifact.

Related use case

A stricter version for regulated teams

For the regulated-team path with auditor-facing proof, see Prove every AI-assisted change was allowed.

Use case pilot

Test regulated AI delivery controls

Bring one workflow, one repo, and the policies your team already follows. Hakama can show where AI-assisted work passes, fails, and leaves proof.

Request a pilot