Routing & Composition

Advisor Consult

Let a lower-cost executor model run the whole task and, at decision points it chooses, consult a stronger advisor model that reads the full transcript and returns guidance only, never actions.

Problem

Running the strongest model on every turn pays frontier prices for work that a smaller model does just as well. Routing each request to a model tier up front does not help, because the hard moments appear in the middle of a run and cannot be predicted from the request. Delegating a hard sub-task to a stronger sub-agent hands over control and forces the parent to summarise its context into a task string, losing the tool results and failed attempts that make the decision hard. Planning once with a strong model and executing with a weak one fixes the plan before the executor has met the problems the plan did not foresee.

Solution

Expose the advisor to the executor as one tool with no meaningful arguments. When the executor calls it, the harness, not the executor, assembles the advisor's input from the full transcript: the system prompt, tool definitions, prior tool calls and their results. The advisor runs as a separate inference pass under its own instructions, with no tools of its own, and returns guidance text such as a plan, a course correction or a recommendation to stop. That text is inserted as the tool result and the executor continues in the same request. The executor decides when to consult; typical moments are before committing to an approach, when an error recurs, and before declaring the task done. Because the advice is guidance rather than a command, the executor still checks it against what it has observed and surfaces a conflict when a recommended step fails or contradicts the files. A per-request cap on consultations bounds cost, and advisor tokens are metered separately from executor tokens so the trade-off stays visible. Where the executor under-consults, a single nudge early in the run, or a forced call at a known decision point, restores the call rate.

When to use

  • Long agentic runs where most turns are mechanical but a few decisions determine the outcome.
  • A cheaper model handles the routine turns well, and a clearly stronger model is available for the same provider or harness.
  • The hard moments cannot be predicted from the request, so up-front routing would either overpay or underperform.
  • The second opinion needs the gathered evidence, including failed attempts, not a summary.

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