DEV Community: Tran Tien Van

TL;DR — Claude Sonnet 5: a practical guide for production teams

Tran Tien Van — Wed, 01 Jul 2026 11:47:27 +0000

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).

Claude Sonnet 5: a practical guide for production teams

Tran Tien Van — Wed, 01 Jul 2026 11:04:09 +0000

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.

Key takeaways

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

📖 Read the full guide on Van Data Team → Claude Sonnet 5: a practical guide for production teams

Governing Agentic AI at Scale: Securing AI-Generated Code in the CI/CD Pipeline

Tran Tien Van — Tue, 30 Jun 2026 10:58:50 +0000

Governing Agentic AI At Scale: Securing AI-Generated Code In The CI/CD Pipeline guide for production teams: compare workflow fit, risk, cost, review burden.

Key takeaways

AI-generated code now belongs inside normal software delivery governance, with extra evidence around agent identity, prompt context, artifact provenance, and approval history.
CI/CD is the right control plane because it already decides what code can build, test, package, deploy, and roll back.
Human review should be risk-based. Low-risk agent changes can pass through policy checks, while dependency, credential, infrastructure, and production-release changes should escalate.
DORA-style metrics still matter, but they are incomplete when dashboards cannot distinguish human-authored changes from autonomous agent activity.
The practical operating model is provenance, signing, policy gates, isolated execution, continuous monitoring, and clear rollback ownership.

📖 Read the full guide on Van Data Team → Governing Agentic AI at Scale: Securing AI-Generated Code in the CI/CD Pipeline