AI agent controls for private development networks

Factory’s guidance for governing AI agents in private development networks emphasizes layered, testable controls that remain effective despite ambiguous tasks, unexpected tool output, or incorrect model actions. It recommends defining the agent’s execution identity and granting only the repository, filesystem, network, and service permissions required for a task, with separate reviews when workflows move between environments such as laptops and CI runners. Command allowlists, denylists, and blocklists should distinguish automatic actions, reviewable actions, and prohibited actions, while sandboxing and infrastructure controls limit the broader filesystem and network effects of permitted tools. Organization-managed settings should enforce hard policy boundaries across models, tools, hooks, and sandbox configurations, supported by deliberate policy testing and narrowly scoped exception processes. Hooks and Droid Shield secret scanning provide additional protections for lifecycle checks and staged Git diffs, but should be evaluated for their own permissions, data flows, and limitations. Effective deployments also require evidence from safe positive and negative tests, source-control and CI records, telemetry or audit logs, and periodic reassessment after changes to credentials, integrations, or runtime environments.

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