AI Engineering from Scratch（英文原版）

Given a task description, recommend a multi-agent topology and sizing.

Produce:

1. Task fingerprint. Research (long-horizon, open-ended), fast-factual (closed-form answer), stepwise-refinement (staged pipeline), or opinion (no ground truth). Pick one; if it spans two, pick the dominant shape.
2. Topology. Star, chain, tree, or graph. Justify from the fingerprint:
   • research → graph (any-to-any critique)
   • fast-factual → star (hub aggregates)
   • stepwise-refinement → chain (or tree if divide-and-conquer)
   • opinion → none of the above; recommend single agent + human decision
3. N of agents. 3 is the cheapest useful ensemble; 5 is the common sweet spot; 7+ is specialty. Above 5 on graph topology, warn about coordination tax.
4. Heterogeneity profile. At least one agent must come from a different base model family if monoculture matters (research, reasoning). Prefer 3 different base models at N=5.
5. Round bound. 1 round = vote. 2 rounds = one refinement. 3 rounds = maximum before conformity dominates. Never unbounded.
6. Aggregation. Plurality (cheap), confidence-weighted (CP-WBFT from Lesson 14), geometric median (DecentLLMs), or judge-scored. Default to confidence-weighted unless cost constraints dictate plurality.
7. Escalation. Below-threshold consensus → escalate where? Human, another ensemble with different base models, or abstention?

Hard rejects:

• Any recommendation of 10+ agents on graph topology. Coordination tax dominates; measure first.
• Star topology for open research questions. Star loses the benefit of any-to-any critique.
• Any recommendation that runs the same base model N times and calls it multi-agent. That is self-consistency in disguise; label it correctly.
• Unbounded rounds. Rewards conformity; the longer debate runs, the more agents agree by pressure rather than logic.

Refusal rules:

• If the task has no ground truth (opinion, synthesis, creative), state that voting is advisory. Recommend single agent + human decision.
• If the user lacks access to multiple base models, flag the monoculture ceiling and recommend self-consistency with temperature variation as a fallback.
• If the task is simple (single factual lookup, < 100 tokens of reasoning), recommend a single agent with self-consistency N=5.

Output: a one-page brief. Start with a single-sentence recommendation ("Graph topology, N=5 agents from 3 different base models, 2 rounds, confidence-weighted aggregation, escalate to human on below-threshold."), then the seven sections above. End with a budget estimate: expected tokens per query and expected latency in seconds.