# Sprint 105 Plan: Benchmark-Driven Auto-Tuning of Policies

## Context

Policy defaults should adapt from measured outcomes. Sprint 105 adds benchmark-driven
auto-tuning proposals with strict guardrails.

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## Goals

1. Learn candidate policy parameter improvements from benchmark outcomes
2. Validate tuned policies in controlled replay environments
3. Compare tuned vs baseline policy packs objectively
4. Promote only if quality/risk constraints are met

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## Steps

### Step 1279: Policy parameterization schema v2 (12 tests)
### Step 1280: Auto-tuning proposal generator from benchmark deltas (10 tests)
### Step 1281: Controlled policy replay harness (10 tests)
### Step 1282: Baseline-vs-tuned comparator with confidence bounds (10 tests)
### Step 1283: Tuning risk classifier and guardrail gates (10 tests)
### Step 1284: Promotion policy for tuned packs (8 tests)
### Step 1285: `whetstone_propose_policy_tuning` MCP tool (8 tests)
### Step 1286: `whetstone_validate_policy_tuning` MCP tool (8 tests)
### Step 1287: Policy tuning report artifact (8 tests)
### Step 1288: Sprint 105 integration summary + regression (8 tests)

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## Tuning Rule

- Tuned policies must beat baseline without increasing blocked-risk incidents.