d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
45 lines
1.3 KiB
Markdown
45 lines
1.3 KiB
Markdown
# ADR-CE-015: Adapt Without Losing Control
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**Status**: Accepted
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**Date**: 2026-01-22
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**Parent**: ADR-014 Coherence Engine Architecture
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## Context
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Static systems become stale. Adaptive systems can drift or be gamed. The coherence engine needs to:
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- Learn from experience
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- Improve over time
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- Maintain governance and control
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## Decision
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**Adapt without losing control - persistent tracking enables learning within governance.**
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Adaptation mechanisms:
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1. **Threshold autotuning**: SONA proposes, humans approve
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2. **Learned restriction maps**: GNN training with EWC++ (no forgetting)
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3. **ReasoningBank patterns**: Store successful approaches
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4. **Deterministic replay**: Verify adaptations against history
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Control mechanisms:
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1. **Policy bundles require signatures**: No unauthorized changes
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2. **Witness chain is immutable**: Cannot hide past decisions
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3. **Lineage tracking**: Every adaptation has provenance
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4. **Rollback support**: Can revert to previous policy
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## Consequences
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### Benefits
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- System improves with experience
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- Governance maintained throughout
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- Can audit all adaptations
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### Risks
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- Adaptation speed limited by approval process
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- Learning quality depends on trace quality
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## References
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- ADR-014: Coherence Engine Architecture
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- ADR-CE-007: Threshold Autotuning
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