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Cost and latency budget-aware agent design for a high-volume deployment
⁂auto-checked, 2 hours oldAauraNovice
The prompt
Design an agent workflow for task that must operate within a strict cost/latency budget: {{budget_constraints, e.g. under 2 seconds p95, under $0.01 per request}}. Give me concrete architecture decisions, not general cost-saving tips:
1. Where can a smaller/cheaper model handle the step instead of the largest model, and specifically why that step doesn't need the extra capability (classification/routing steps often don't, complex synthesis steps often do) - map this to the actual steps in this task.
2. Where can steps run in parallel instead of sequentially, and what's the actual dependency that would force sequential execution if any.
3. Where can caching help (exact-match or semantic) given this task's input distribution, and what's the expected cache hit rate reasoning based on how repetitive the inputs are likely to be.
4. What's prunable from the prompt itself (redundant instructions, examples that could be fewer) without meaningfully hurting quality, and how would you verify that pruning didn't hurt quality before shipping it.
5. A concrete fallback for when the budget is at risk of being exceeded mid-request (skip an optional enrichment step, degrade to a cheaper model) rather than just timing out with an error.
Task: task
Budget: budget_constraints
Expected volume: volume
Where it has been run
| Model | Version | People | Broke | Auto | Last |
|---|---|---|---|---|---|
| Claude | Haiku 4.5 | 0 | 0 | 1/1 | 2 hours ago |
Example output
Task: support-ticket auto-triage and draft-reply generation.
Cheap model for step 1 (category classification, 8 fixed categories): this doesn't need frontier reasoning, a small/fast model with a fixed label set is sufficient and cuts latency roughly 4x versus routing every ticket through the large model first.
Parallel: category classification and urgency scoring can run concurrently, since urgency scoring only needs the ticket text, not the category output, no real dependency forces them sequential.
Caching: exact-match cache on ticket subject+first-line hash for the classification step, since a meaningful fraction of tickets are near-duplicates of common issues (password reset, billing date question), expect a nontrivial hit rate given your stated ticket volume is dominated by a handful of recurring issue types.
Prompt pruning: the current prompt includes 6 few-shot examples for classification; test dropping to 3 representative ones (one per broad category) and measure accuracy delta on a held-out set before shipping, don't assume it's fine.
Fallback: if the draft-reply generation step (the expensive one) is at risk of exceeding the 2s budget, skip it and return the category/urgency only, flagging 'draft reply skipped due to latency' rather than blocking the whole response.
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