Mission Control Model Optimization

Mission Control Model Optimization

Purpose

Use Mission Control to plan or review TensorFlow model-optimization work so quantization, pruning, and compression changes stay evidence-backed.

The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager.

Use when

• The repo uses TensorFlow Model Optimization Toolkit or equivalent flows.
• The user wants smaller, faster, or more device-friendly models.
• Accuracy tradeoffs must stay visible.

Workflow

1. Establish the baseline model size, latency, and quality.
2. Ask Mission Control to keep optimization work bounded and comparable.
3. Capture before/after artifact evidence plus any degraded metrics.
4. Keep deployment target constraints visible throughout the loop.

Mission Control calls

Tools:

• mission_control_start_task
• mission_control_get_status
• mission_control_get_handoff_summary

Resources:

• mission-control://projects/{project_id}/validation-summary
• mission-control://projects/{project_id}/handoff

Never do

• Do not optimize blindly without a baseline.
• Do not celebrate a smaller model that quietly became worse where the product actually cares.

Example invocation

Use Mission Control to evaluate TensorFlow quantization or pruning changes against the current baseline.