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After task completion, record whether the mode was appropriate. Task succeeded with direct mode → reinforce. Task failed with direct mode → entropy was underestimated → learn. Over time, entropy estimates improve automatically via #31 (immune memory).
For Agents Working On This
This depends on #31 (memory evolution) and #32 (energy activation) for the entropy signals and graph propagation. Don't start this until those are in place. Read the full spec in #71. Fixed workflows remain as manual override — backward compatible.
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Entropy-Adaptive Coordination: Dynamic Agent Orchestration
Issue: #71 | Priority: P3 | Depends on: #31, #32, #26 | Origin: Crystal OS physics-based coordination modes
The Problem
KERNEL has 3 fixed workflows (feature, bugfix, refactor) that define nearly identical agent sequences. They route by task TYPE, not task UNCERTAINTY.
This is wrong for many cases:
The Proposal
Replace type-based workflow selection with entropy-based coordination modes inspired by Crystal OS physics:
Entropy Signals
Task entropy computed from:
The Learning Loop
After task completion, record whether the mode was appropriate. Task succeeded with direct mode → reinforce. Task failed with direct mode → entropy was underestimated → learn. Over time, entropy estimates improve automatically via #31 (immune memory).
For Agents Working On This
This depends on #31 (memory evolution) and #32 (energy activation) for the entropy signals and graph propagation. Don't start this until those are in place. Read the full spec in #71. Fixed workflows remain as manual override — backward compatible.
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