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feat(moa): default advisor fanout to user_turn — the cheapest cadence

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Flips the default fan-out cadence from per_iteration (advisors re-run on
every tool iteration, multiplying advisor spend by tool-loop depth) to
user_turn (advisors run once on the first message of each user turn; the
acting aggregator works the rest of the tool loop with that turn's
advice). Until per-mode benchmarks justify a costlier default, MoA
defaults to the cheapest, lowest-impact cadence (#67199).

One default for everyone — no split legacy/new-preset semantics; presets
that want per-step advising set fanout: per_iteration explicitly. All
three modes (user_turn / per_iteration / every_n:N) remain selectable;
every_n:1 still collapses to per_iteration (semantic identity), while
unparseable values now fall to user_turn (the default).

Docs updated with a default-change note; the per-iteration rerun test
pins its mode explicitly.

Co-authored-by: skyer-flyyy <188930297+skyer-flyyy@users.noreply.github.com>
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Teknium 2026-07-23 20:11:57 -07:00
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29
website/docs/user-guide/features/mixture-of-agents.md
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@ -134,14 +134,16 @@ Leave it unset (or `0`/blank) to keep the prior uncapped behavior.
### Advisor cadence with `fanout`
By default the advisors re-run on **every tool iteration** (`fanout:
per_iteration`), so their advice always tracks the latest tool results — at
the cost of multiplying advisor latency and spend by the number of tool calls
in a turn. Two alternative cadences trade freshness for speed:
By default the advisors run **once per user turn** (`fanout: user_turn`) —
they synthesize plan-level advice on the first message of the turn, then the
acting aggregator works through the rest of the tool loop alone. This is the
cheapest cadence: advisor cost does not multiply with the number of tool
calls in a turn. Two alternative cadences trade cost for advice freshness:
- `fanout: user_turn` — advisors run **once per user turn**; every tool
iteration after that reuses the same upfront advice (the original MoA
shape: synthesize a plan, then let the acting model work).
- `fanout: per_iteration` — advisors re-run on **every tool iteration**, so
their advice always tracks the latest tool results — at the cost of
multiplying advisor latency and spend by the number of tool calls in a
turn.
- `fanout: every_n:3` — the middle ground: advisors run on the **first**
iteration of each user turn and then every **3rd** tool iteration (any
`N >= 2` works). Iterations in between reuse the cached guidance from the
@ -154,17 +156,24 @@ in a turn. Two alternative cadences trade freshness for speed:
```yaml
moa:
presets:
fast:
fresh:
reference_models:
- provider: openrouter
model: anthropic/claude-opus-4.8
aggregator:
provider: openrouter
model: openai/gpt-5.5
fanout: every_n:3 # advisors refresh every 3rd tool iteration
fanout: per_iteration # advisors refresh on every tool iteration
```
Unknown or malformed values fall back to `per_iteration`.
Unknown or malformed values fall back to `user_turn`.
:::note Default change
Prior to July 2026 the default cadence was `per_iteration`. The default is
now `user_turn` — the cheapest, lowest-impact cadence — until per-mode
benchmarks justify a costlier default. Presets that want per-step advising
back set `fanout: per_iteration` explicitly.
:::
### Privacy filter for advisor outputs