Trust

Agents produce inside this structure. They never own what they produce. A senior does.

• Your code & repositories

  Your repository, your IP, from day one. We work inside your repositories, never in a Bowery fork or a copy, so there is no separate place the code has to be returned from. Ownership is total and unconditional, during the engagement and after it.

• Access & credentials

  Our engineers work under accounts you issue, in your own systems. You grant access under contract, and Bowery holds a collaborator role that can manage its own team membership. The super admin role stays with you at all times, so access can always be revoked without us. Multi-factor authentication is required.

• Secrets & production

  Agents never have autonomous production authority. AI prepares and executes inside controlled boundaries, and a human authorizes production. Six controls govern it:

  • Secrets stay protected. Credentials, API keys, tokens and production connection strings stay out of source code, out of repositories, and out of AI context. They remain in your approved secret management and infrastructure, and access follows least privilege: what the role requires, for as long as it requires it.
  • Production is a protected boundary. Agents work in development, test, staging and CI, which are controlled environments. Access to production is explicitly granted, scoped and justified by the engagement. It is never assumed from repository access.
  • No autonomous production authority. An agent can prepare production work: code, tests, infrastructure changes, deployment configuration, migrations, rollback plans. It cannot approve its own output, grant itself permissions, bypass a gate, or make an irreversible production change on its own judgment.
  • Humans authorize what ships. Production-critical actions require a human-controlled gate, and a named engineer is accountable for the change and its outcome. That engineer has to have enough information to understand what they are approving. A rubber stamp is not oversight.
  • Actions remain attributable. Production-related activity stays traceable through the controls your environment provides: what changed, who or what initiated it, which human authorized it, and when. Agent activity never obscures the accountable human.
  • Your controls come first. Bowery operates within customer-granted permissions and does not weaken your security controls to make an agent workflow faster. Where your requirements are stricter than our baseline, yours prevail. Security controls take precedence over delivery speed.
• Customer data

  Code only, without exception. Work happens in test environments against test data, so your end users’ real records are not part of the engagement. Where regulated data exists, five controls govern it:

  • Data minimization. Agents receive only the context the task needs. Where possible, PII and PHI are removed, anonymized, tokenized, or replaced with synthetic data before entering an AI workflow.
  • Controlled AI boundary. Not every model may process every kind of data. For sensitive workloads we define which providers and models are approved, their retention and training policies, where processing happens, and which data may not leave your environment at all.
  • Least-privilege access. Engineers and agents reach only the systems and data their function requires. Separate credentials, minimum permissions, and secrets kept out of prompts and repositories.
  • Auditability. Access and consequential actions have to be traceable, especially where an agent touches production, a database, or a system holding sensitive information.
  • Human ownership. An agent is never the final party responsible for a sensitive decision. A named engineer stays accountable for the workflow, and critical operations pass through human gates.
• AI providers

  Bowery uses controlled commercial AI environments where customer data is not used for model training by default, minimizes the context sent to providers, and applies documented retention and provider-routing controls per engagement. Six controls govern it:

  • Approved providers only. Anthropic and OpenAI, through controlled commercial environments. Client code is never routed through personal, free, or consumer AI accounts, or through tools that have not been approved.
  • No training by default. Client code is processed only through commercial configurations in which provider model training is off by default. Voluntary data-sharing and model-improvement opt-ins stay disabled for any environment that touches client code.
  • Minimum necessary context. Agents receive only the code and context the task requires, and no more.
  • No production customer data. AI engineering workflows run on code and controlled test environments, not on your production datasets. Credentials, secrets, PII and PHI do not enter model context.
  • Retention is controlled. Provider-side retention is minimized and documented per engagement. Stricter retention configurations, including data-residency and dedicated provider routes, can be adopted where an engagement requires them and the route supports them.
  • The route is documented. Provider, product, endpoint, account, and the applicable retention configuration are recorded per engagement. Two routes running the same model do not necessarily share a retention policy, so the route is treated as part of the security boundary.
• Offboarding

  Access is revoked by your administrator, by the same route it was granted. Local working copies are removed when the engagement ends, and the confidentiality obligations in the NDA continue past it. The governance we leave running keeps the record: repository monitoring and stack-level logging show what was touched, and by whom.

• Named ownership on every merge
• Test coverage
• Review history
• Merge log
• Delivery metrics
• Monthly Ops reporting

Every merge is typed, tested, documented, and senior-reviewed. The trail is there because the system produces it, not because we assembled it for you.