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Managers operate with graduated autonomy. The trust progression model ensures that autonomous agents start conservatively and only gain more authority as they prove reliable.

Three trust levels

Observer

The Manager monitors and logs but takes no action. This is the default starting level.
  • Detects issues and anomalies
  • Records observations in the activity log
  • No user notifications beyond the log

Advisor

The Manager detects issues, proposes specific fixes, and asks for your confirmation before acting.
  • Sends notifications with proposed actions
  • Waits for explicit approval before executing
  • Tracks acceptance rate to build trust score

Autonomous

The Manager executes fixes automatically without requiring confirmation.
  • Acts immediately on detected issues
  • Logs all actions for audit trail
  • Can be reverted to Advisor at any time

Why this matters

Autonomous infrastructure agents need guardrails. Giving an AI full control from day one is risky. The trust progression model:
  • Builds confidence — You see the Manager’s judgment before trusting it
  • Prevents damage — Bad suggestions are caught before execution
  • Creates audit trails — Every action is logged regardless of trust level
  • Enables rollback — You can reduce trust level at any time

Further reading

Guard setup

Configure Guard’s trust level

Architecture

How Managers fit into the overall system