ADR-172: Fable Advisor Harness via claude -p (Cost-Disciplined Judge + Reflector)

ID: ADR-172 Status: Proposed — implemented on feat/agenticow-integration (ships in 3.21.0) Date: 2026-07-04 Authors: rUv (drafted with Claude Code) Related ADRs:

• ADR-171 (Provenance-tiered oracle — Tier-2 uses this harness as the judge)
• ADR-150 (MetaHarness — GEPA reflective-mutation is the optimization consumer)
• ADR-143 (Model routing / cost tiers — the cost discipline here is the same concern at a higher tier)

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1. Context

Two SOTA loops want a frontier model in the loop, not just at the endpoints:

• Distillation (ADR-171 Tier 2): judge whether a cheap-model completion actually solved a task — a smarter labeler than structural confidence.
• Optimization (GEPA, ADR-150): GEPA's signature trick is natural-language failure diagnosis from execution traces feeding the next mutation — not a scalar fitness. metaharness_evolve mutates without it.

Both are the same primitive: a headless frontier-model advisor. Fable 5 (claude-fable-5) via claude -p provides it.

The load-bearing constraint is cost. Measured: a trivial claude -p --model claude-fable-5 call from the project directory costs $1.56 — it loads ruflo's CLAUDE.md context (56k cache-creation tokens). At a 100-trajectory corpus that is ~$150. Naive integration is unusable.

2. Decision

A single cost-disciplined fable-harness.ts service wrapping claude -p, with two entry points and three mandatory cost controls.

2.1 Cost controls (all required)

1. Clean cwd — spawn claude -p from a fresh empty temp directory so no project CLAUDE.md / settings load. Measured effect: $1.56 → $0.34 (cache-creation 56k → 3.7k tokens).
2. Batching — N items per call (default 20). The per-call base amortizes to ~$0.02/item.
3. --append-system-prompt for the role + --max-budget-usd cap + --output-format json. Opt-in, off by default.

Combined: $1.56/item naive → ~$0.02/item disciplined — the difference between a demo and a usable loop.

2.2 Entry points

• judgeCompletions(items[]) → [{id, resolved, confidence, reason}] — ADR-171 Tier 2. (Probe: correctly returned resolved:true for an applied patch, false for "I am not sure how to do this.")
• reflectFailures(items[]) → [{failureClass, diagnosis, mutationHint}] — GEPA reflective-mutation feed for evolve/optimization.

2.3 Safety

• No Fable call unless the caller explicitly opts in and passes a budget cap.
• ADR-150 graceful: no claude binary / budget exhausted / non-JSON reply → structured degraded result, never a throw.
• Tests mock claude -p (child_process) — CI never spends. One live smoke behind RUFLO_FABLE_LIVE=1 for humans.

3. Consequences

• One harness serves both the distillation judge and the optimization reflector — the frontier-model-in-the-loop SOTA pattern, cost-bounded.
• Fable's judgment is Tier 2, never Tier 1 — an LLM judge is a smarter proxy, not ground truth, and cannot outrank real execution (ADR-171) or clear a promote unless explicitly accepted.
• Cost is measured and enforced, not hoped: the batch-mode default + spend cap are hard gates in the release checklist.

4. Alternatives rejected

• Fable per item from the project dir: $1.56/item — ~75× the disciplined cost. The whole ADR exists to reject this.
• A cheaper model as judge: the judge must be at least as capable as the tier it grades; using the cheap tier to grade itself is circular.
• Skip the reflector, keep scalar-only evolution: leaves GEPA's proven advantage on the table (SOTA-researcher-flagged gap).