docs(mma): Draft Execution Clutch & Linear Debug Mode section

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2026-02-24 22:34:19 -05:00
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@@ -15,7 +15,15 @@ To align with the 4-Tier Architecture, the Conductor extension must implement **
3. **Amnesiac Context Management:** Incorporate lifecycle hooks (`before_tool_call`, `after_tool_call`) to clean up unnecessary tool outputs from the active memory array, only keeping the 50-token summaries of execution outcomes.
## 3. Execution Clutch & Linear Debug Mode
*To be drafted. Focus: Evaluating current execution workflows and proposing mechanisms for manual step-through and auto modes.*
### Current Evaluation
Conductor currently employs an iterative, fire-and-forget `execute_tasks` workflow where each `replace`, `write_file`, and `run_shell_command` is done sequentially via its prompt instructions. While autonomous, the user's only control mechanism during rapid tool-calling is the standard CLI prompt interruption, which may leave tracked artifacts in an inconsistent state or execute runaway hallucinated loops.
### Proposed Upgrade Strategy
To enforce precise developer control, Conductor should natively embed a **Human-In-The-Loop Execution Clutch**:
1. **Interactive Checkpoints (Trust Levels):** Use extension hooks like `before_tool_call` to intercept payload executions based on heuristic models. Tools like `replace` might trigger an interactive payload editor (`vim` / CLI editor plugin) before applying the JSON parameters, ensuring full developer review.
2. **Global Linear Mode Flag:** Implement a `gemini conductor:implement --step` flag. This configures the engine to pause execution and prompt the user using `ask_user` natively after every major milestone, allowing validation of file diffs and tool payloads before resuming.
3. **Rollback Mutators:** Provide quick access commands (e.g., via `after_tool_call`) to reject the change, auto-restoring the last known file state, and feeding the error/feedback directly back to the model without breaking the run loop.
## 4. Multi-Model/Sub-Agent Delegation
*To be drafted. Focus: Evaluating reliance on single models and proposing a design for delegating tasks via Gemini CLI Sub-agents and Skills.*