| name | model-tiering |
| description | Cost-efficient model tiering — the advisor pattern (a cheap executor consults a stronger model rarely) and the orchestrator pattern (a strong model plans while cheaper workers execute). Use when choosing which model runs a session vs its subagents, when cost matters on long agentic work, or when the user mentions "Fable", "advisor", "model tiering", "token cost", "cheaper model", or asks which model should drive a workflow. |
Model Tiering
Two tiers, two directions of pull. Bill the volume tokens at the cheaper rate and spend the strongest model only where a single decision changes the outcome.
- Advisor tier — the strongest model available (Claude Fable 5)
- Executor tier — a fast, capable model (Claude Sonnet 5)
Reference numbers (SWE-bench Pro): a Sonnet 5 executor with a Fable 5 advisor reaches ~92% of Fable 5's solo score at ~63% of the price, with the advisor consulted roughly once per task. Official reference: https://platform.claude.com/docs/en/agents-and-tools/tool-use/advisor-tool
Pattern 1 — Advisor: the executor pulls intelligence down
Run the session on the executor tier; dispatch the advisor agent (pinned to the strongest tier) rarely and deliberately:
- Once early — after orientation (finding files, reading what's there) but BEFORE substantive work: writing, committing to an interpretation, building on an assumption
- When stuck — recurring errors, an approach that is not converging, results that don't fit
- Once before declaring done on hard tasks — after writes and test output exist; make the deliverable durable (commit it) before the consult
Treat the advice with serious weight: deviate only on empirical failure or primary-source contradiction. If your evidence points one way and the advisor points another, don't silently switch — surface the conflict in one reconcile consult ("I found X, you suggest Y — which constraint breaks the tie?").
Target ~1–3 consults per task. On short reactive tasks where the next action is dictated by output just read, skip the advisor — its value is highest before the approach crystallizes.
Pattern 2 — Orchestrator: the strong model pushes work down
Run orchestrating sessions (/orchestrate, /orchestrate-loop, /plan-phase, /execute-phase) on the strongest tier and let workers execute on the executor tier. The orchestrator plans, decomposes, dispatches, and verifies; workers burn the volume tokens at the lower rate.
Pin worker models per agent via the plugin's configure command (it writes model: into the agent frontmatter). Keep spec-reviewer and the final verification gate on a strong tier — verifier quality bounds loop quality.
Choosing
| Workload | Pattern |
|---|
| Mostly mechanical, occasional hard decisions | Advisor — one context, rare consults |
| Sustained planning and decomposition quality needed | Orchestrator — the strong model steers throughout |
Long loops (/orchestrate-loop) | Orchestrator, with advisor consulted on stall |
The patterns compose: an orchestrating session on the executor tier can still pull advisor for its hardest decisions.
API-level advisor (built harnesses)
For agents built directly on the Claude API (e.g. draht's own harness code), the advisor exists as a server-side tool: {"type": "advisor_20260301", "name": "advisor", "model": "<advisor model id>"} with beta header advisor-tool-2026-03-01. The executor calls advisor() with no input; the server forwards the full transcript to the advisor model and only the advice text returns. Cap cost with max_uses and max_tokens on the tool definition; trim advice length with an inline user-message request ("Advisor: please keep your guidance under 80 words").
