Objective
Evaluate whether a human and an external supervisory AI can productively oversee complete autonomous coding-harness sessions as organizational units, while each worker harness retains its own agents, providers, validation, and review systems.
Finish Line
Offline evidence determines whether an external read-only supervisory AI adds useful early drift detection without excessive intervention noise, attention cost, or token overhead.
Current Status
State: Intentionally parked research direction with no automatic activation trigger. It is not part of the Codex Lab Private Dogfood milestone and must not delay release.
The concept remains preserved here and related to #121, #298, and #150. Closing the private-dogfood milestone or accumulating sessions does not start this work by itself. After release, the user must explicitly promote this issue before any experiment, implementation, or new child issue begins.
If promoted, the first step is still an offline/read-only replay with no live authority. Until then, no current roadmap, milestone, gate, handoff, or implementation prompt should pull this concept into active scope.
Next action: none until explicit user promotion after private dogfood evidence exists.
Blocked by: no native issue blocker; intentionally parked by product decision.
Last verified: August 6, 2026.
Research Shape
- Treat each worker harness as an autonomous organizational unit rather than a subagent call.
- Keep the supervisor outside the worker's context and write plane.
- Consume structured events and bounded evidence, never an unbounded transcript tail.
- Begin offline or read-only; grant no steering, pause, file-write, merge, or release authority.
- Measure intervention quality and human attention before adding product UI or authority levels.
Acceptance Criteria
Relationships
Next Action
Do nothing until the private dogfood milestone closes and enough real sessions exist. Then design one offline replay experiment before considering a live shadow supervisor.
Blocked by: no native issue blocker; intentionally waiting for released-product evidence.
Objective
Evaluate whether a human and an external supervisory AI can productively oversee complete autonomous coding-harness sessions as organizational units, while each worker harness retains its own agents, providers, validation, and review systems.
Finish Line
Offline evidence determines whether an external read-only supervisory AI adds useful early drift detection without excessive intervention noise, attention cost, or token overhead.
Current Status
State: Intentionally parked research direction with no automatic activation trigger. It is not part of the
Codex Lab Private Dogfoodmilestone and must not delay release.The concept remains preserved here and related to #121, #298, and #150. Closing the private-dogfood milestone or accumulating sessions does not start this work by itself. After release, the user must explicitly promote this issue before any experiment, implementation, or new child issue begins.
If promoted, the first step is still an offline/read-only replay with no live authority. Until then, no current roadmap, milestone, gate, handoff, or implementation prompt should pull this concept into active scope.
Next action: none until explicit user promotion after private dogfood evidence exists.
Blocked by: no native issue blocker; intentionally parked by product decision.
Last verified: August 6, 2026.
Research Shape
Acceptance Criteria
Relationships
Next Action
Do nothing until the private dogfood milestone closes and enough real sessions exist. Then design one offline replay experiment before considering a live shadow supervisor.
Blocked by: no native issue blocker; intentionally waiting for released-product evidence.