Agentix Labs AI agent governance

Safe AI Agent Read/Write Access to Company Systems

AI agents become useful when they can read context and take action. The implementation challenge is giving them enough access to complete work while keeping permissions scoped, observable, and reversible.

Access model

  • Start with read-only retrieval before write actions.
  • Scope tools by workflow, record type, and business owner.
  • Separate test, staging, and production credentials.
  • Document every connector, API key, and MCP server purpose.

Approval design

  • Require human review for external messages, financial updates, risky CRM changes, and irreversible actions.
  • Let low-risk drafts, summaries, and routing updates run with lighter review.
  • Use confidence thresholds and policy checks before action.
  • Keep escalation paths visible in the run log.

Observability

  • Log inputs, retrieved records, tool calls, outputs, approvals, failures, and rollback actions.
  • Track who approved each live write.
  • Capture before/after state for edited records when possible.
  • Review failures weekly during the pilot.

Rollout path

  • Pilot one workflow with one owner and one tool group.
  • Expand after accuracy, recovery, and owner satisfaction are proven.
  • Remove unused permissions before adding new ones.
  • Reassess access whenever the workflow changes.

How to score it

Give one point for every checked item. Then use the result to decide what happens next.

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