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+# ADR-CE-015: Adapt Without Losing Control
+
+**Status**: Accepted
+**Date**: 2026-01-22
+**Parent**: ADR-014 Coherence Engine Architecture
+
+## Context
+
+Static systems become stale. Adaptive systems can drift or be gamed. The coherence engine needs to:
+- Learn from experience
+- Improve over time
+- Maintain governance and control
+
+## Decision
+
+**Adapt without losing control - persistent tracking enables learning within governance.**
+
+Adaptation mechanisms:
+1. **Threshold autotuning**: SONA proposes, humans approve
+2. **Learned restriction maps**: GNN training with EWC++ (no forgetting)
+3. **ReasoningBank patterns**: Store successful approaches
+4. **Deterministic replay**: Verify adaptations against history
+
+Control mechanisms:
+1. **Policy bundles require signatures**: No unauthorized changes
+2. **Witness chain is immutable**: Cannot hide past decisions
+3. **Lineage tracking**: Every adaptation has provenance
+4. **Rollback support**: Can revert to previous policy
+
+## Consequences
+
+### Benefits
+- System improves with experience
+- Governance maintained throughout
+- Can audit all adaptations
+
+### Risks
+- Adaptation speed limited by approval process
+- Learning quality depends on trace quality
+
+## References
+
+- ADR-014: Coherence Engine Architecture
+- ADR-CE-007: Threshold Autotuning