d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
47 lines
1.2 KiB
Markdown
47 lines
1.2 KiB
Markdown
# ADR-CE-007: Thresholds Auto-Tuned from Production Traces
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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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Fixed thresholds become stale as:
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- System behavior evolves
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- New edge types are added
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- Domain characteristics change
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Manual tuning is expensive and error-prone.
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## Decision
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**Thresholds auto-tuned from production traces with governance approval.**
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Process:
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1. **Collect traces**: Energy values, gate decisions, outcomes
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2. **Analyze**: SONA identifies optimal threshold candidates
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3. **Propose**: System generates new PolicyBundle with updated thresholds
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4. **Approve**: Required approvers sign the bundle
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5. **Deploy**: New thresholds become active
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Constraints:
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- Auto-tuning proposes, humans approve
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- Changes tracked in audit log
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- Rollback supported via new PolicyBundle
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## Consequences
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### Benefits
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- Thresholds adapt to changing conditions
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- Governance maintained (human approval required)
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- Historical analysis enables data-driven decisions
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### Risks
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- Bad traces lead to bad proposals
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- Approval bottleneck if too many proposals
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## References
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- ADR-014: Coherence Engine Architecture, Section 6
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- ADR-CE-015: Adapt Without Losing Control
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