Every output is checked, every action is logged.

Every draft an agent produces is evaluated against five categories of policy before it can be surfaced to your team. The categories are configurable per organization and per agent.

Voice matchcompares the draft against the leader's established voice — the writing samples we gathered when the agent was configured, plus the agent's history of approved drafts. A draft that drifts in tone, vocabulary, or sentence structure is flagged.

Claim verification checks for factual assertions the draft cannot support — fabricated statistics, invented quotes, unverifiable specifics. Drafts making strong claims without backing are flagged for review.

Policy compliance enforces explicit customer rules. Common examples: no client names, no revenue figures, no commentary on specific competitors, required disclaimers on certain content types. These rules are stored as data, not code — they can be changed without a deploy.

Confidentiality scans for sensitive information that should not leave the company — internal project codenames, customer identities not intended for public reference, financial details, anything matching patterns you define as confidential.

Brand consistency checks the draft against organization-wide voice standards — distinct from voice match, which is per-leader. This is the check that catches a draft sounding too casual for a serious brand, or vice versa.

Each check returns one of three results: pass, warn, or block. Pass means the draft proceeds to your approval queue. Warn means the draft proceeds, but the reviewer sees a note about what was flagged. Block means the draft does not enter the approval queue at all — it is stored separately with the violation reason, and platform admins are notified.

The severity of each check is configurable. An organization-wide rule might block on confidentiality violations but only warn on voice drift. A high-risk agent (a public-facing CEO's LinkedIn) might block on more checks than a low-risk agent (internal newsletter).

When the governance layer itself is unavailable — network failure, service down, malformed response — the system fails closed. No draft is produced. The agent run is held back, platform admins are alerted, and your team sees no output that bypassed governance because governance was offline.

This is the opposite of how most AI tools handle a failed safety check. Many fail open, assuming the rare failure of the check is a smaller risk than blocking legitimate output. For enterprise content under a leader's name, we have made the opposite call: blocking a legitimate post is recoverable; publishing a problematic one is not.

Policies are stored as data in the database, not embedded in code. There are two scopes: organization-wide policies that apply to all agents, and agent-specific policies that override the organization rules for individual agents.

Platform admins configure these policies on your behalf. Your team can read the current policy set at any time. Direct customer editing of policies is on the roadmap; today, changes go through us so we can verify the rule does what you intended before it takes effect.

Two audit logs exist, kept in sync. The first records what happened inside DeployCo: every agent run, every draft, every approval decision, every change a platform admin made. The second records what the governance layer evaluated and how: the input it received, the policies that applied, the verdict per check, the reason for the verdict.

A compliance officer auditing an agent's output can ask, for any specific draft ever produced: who configured this agent, what did the agent generate, what governance checks ran, what did each check decide, who approved it, what edits did they make, and when was it published. Every one of those questions has an answer recorded in the logs, indefinitely.

This is not a content moderation system in the consumer sense. We are not filtering for hate speech or graphic content at scale — that is a different problem with different tools. We are filtering professional business output against a specific customer's standards.

This is also not a substitute for human approval. Governance reduces the rate of bad drafts reaching your reviewer; it does not eliminate them. The approval step exists precisely because no automated check is perfect.