# From Prototype to Production: Engineering Agents with Agent DLC

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-11-12T15:38:47.000Z  
> **Category:** PRODUCT_LAUNCH  
> **Impact Score:** 130/100  
> **Primary Source:** [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/from-prototype-to-production-engineering-agents-with-agent-dlc)  
> **Canonical Citation:** [https://spidits.com/timeline/from-prototype-to-production-engineering-agents-with-agent-dlc](https://spidits.com/timeline/from-prototype-to-production-engineering-agents-with-agent-dlc)

## Executive Summary
Agents stall between demo and release when teams lack a repeatable way to measure how good an agent is and whether the last change helped.

## Why It Matters (Strategic Analysis)
Traditional CI/CD pipelines fail for LLM agents because aggregate metrics often mask underlying coverage problems and non-deterministic execution paths. Establishing a verifiable gold standard bundle of criteria and thresholds bridges the gap between raw prototyping and reliable production releases.

## Referenced Coverage & Sources
- **[AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/from-prototype-to-production-engineering-agents-with-agent-dlc)**: From prototype to production: Engineering agents with Agent DLC — _Agents stall between demo and release when teams lack a repeatable way to measure how good an agent is and whether the last change helped. This post covers..._

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*Synthesized by SPIDITS AI Market Intelligence Desk. Track live AI news, model releases, and funding: [https://spidits.com](https://spidits.com)*