---
format: "aidr-story-markdown/v1"
id: "dcc3893e53f42774b02e9ab89d63dea672c03cefd38455cd9f5c18c8595093e9"
canonical_url: "https://aidr.today/dcc3893e?lang=en"
title: "From prototype to production: Engineering agents with Agent DLC"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-12T15:38:47.000Z"
category: "Tools"
topics: ["amazon","agent","devtools"]
source_urls: ["https://aws.amazon.com/blogs/machine-learning/from-prototype-to-production-engineering-agents-with-agent-dlc/"]
summary: "Agent DLC provides one lifecycle, two loops, and one explicit standard for judging every change. This addresses issues where agents stall between demo and release due to a lack of repeatable measurement. The post covers three agent properties that break traditional CI/CD and a case where an aggregate metric hides a coverage gap."
---

# From prototype to production: Engineering agents with Agent DLC

> [Open the canonical story](<https://aidr.today/dcc3893e?lang=en>)

**Published:** 2026-11-12T15:38:47.000Z
**Category:** Tools
**Topics:** amazon, agent, devtools

## Summary

Agent DLC provides one lifecycle, two loops, and one explicit standard for judging every change\. This addresses issues where agents stall between demo and release due to a lack of repeatable measurement\. The post covers three agent properties that break traditional CI/CD and a case where an aggregate metric hides a coverage gap\.

## Sources

- [Story source](<https://aws.amazon.com/blogs/machine-learning/from-prototype-to-production-engineering-agents-with-agent-dlc/>)