preferences-adaptive-planning

AI agent planning depth is optimized by applying adaptive planning and control theory principles, dynamically adjusting planning horizons based on project complexity and agent capacity to maximize validated product increments.

Replanning triggers are determined by evaluating backlog task counts and validation gate outcomes, using heuristics from queue economics and control theory to decide when to adjust the workflow.

Control theory applies to AI agent workflow optimization through Model Predictive Control (MPC) and Viable System Model (VSM) concepts, which guide buffer sizing and validation gate placement to minimize waste.

Yes, a DAG-based issue tracker is required to execute adaptive planning, as it structures the task dependencies needed for AI coding agents to process replanning and validation triggers effectively.

Buffer sizing should be adjusted dynamically during the planning cycle by assessing project complexity using Cynefin domains, ensuring sufficient capacity for validation and minimizing wasted effort.

Stigmergic coordination improves AI agent task execution by allowing agents to coordinate indirectly through environment updates, integrating with queue economics to maximize releasable product increments.