What is Mirror AI Field Engineer?
Mirror AI Field Engineer is a reasoning layer built into Mirror OS. It operates exclusively on evidence from Mirror Ledger Engine to help you understand, explain, and improve AI behavior.
Think of it as a senior field engineer who observes evidence, explains root causes, and suggests improvements — but never touches the production system without your explicit approval.
The key distinction:
"AI Field Engineer provides understanding and recommendations. Mirror Trust Layer provides enforcement. They are intentionally separate."
What It Can Do
Explain
Why was this decision blocked, rerouted, or flagged? Get clear explanations tied to specific policies and evidence.
Diagnose
Is this a policy issue, model issue, or usage drift? Understand root causes without guessing.
Recommend
What policy or configuration should be adjusted? Receive actionable suggestions with confidence scores.
Simulate
What would happen if different rules were applied? Test changes before deploying them.
How Teams Use It
Customer Support
Help your team understand why specific AI decisions were made. Reduce escalations with clear explanations.
Deployment Support
Get guidance on configuring policies, setting up integrations, and optimizing your Mirror OS deployment.
Governance Tuning
Identify policy gaps, adjust thresholds, and improve governance effectiveness over time.
Hard Boundaries
These constraints are not configurable. They exist to ensure AI Field Engineer remains a reasoning layer, not an autonomous agent.
Cannot modify policies
All policy changes require human approval through explicit change control.
Cannot change routing
Routing decisions are made by Trust Layer and Gateway, not the reasoning layer.
Cannot override Trust Layer decisions
Enforcement decisions are final. AI Field Engineer can explain them, not change them.
Cannot take real-time actions
All recommendations are advisory. Humans decide what to implement.
Every recommendation requires human approval
AI Field Engineer outputs are advisory. All changes go through explicit approval workflows with full audit trails.
Why "Field Engineer" and Not "Agent" or "Copilot"?
We deliberately avoid terms like "autonomous agent" or "AI copilot" because they imply capabilities that would be inappropriate for governance infrastructure.
A Field Engineer...
- Observes evidence
- Explains root causes
- Suggests actions
- Waits for approval
- Documents everything
An "Agent" implies...
- ✕Autonomous decision-making
- ✕Self-modification
- ✕Direct system access
- ✕Independent action
- ✕Unsupervised operation