TL;DR — Claude Sonnet 5 is best treated as an agent workflow model. See specs, setup steps, guardrails, examples, and review checks for safer production use today.
The gist, in a few bullets:
- Use it first on bounded workflows where planning, tool use, and review checkpoints matter more than one-shot text generation
- Recheck token budgets before migration: Anthropic's Platform Docs say the model has a 1M token context window and 128k max output tokens, while the new tokenizer produces approximately 30% more tokens for the same input text
- Keep tool permissions narrow until the workflow passes evaluation on real examples, including failure cases and rollback checks
- Measure review burden, latency, token spend, skipped steps, and unsupported claims, not only final answer quality
- Move to production only when human review effort decreases without increasing customer, revenue, compliance, or infrastructure risk
I put the full walkthrough — examples, trade-offs, and the review checklist — on Van Data Team → Claude Sonnet 5: a practical guide for production teams
How are you handling this in your own stack? Keen to hear what's working (or not).
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