AWS AI-DLC Platform 把分散的 AI Agent 開發收斂到單一平台：Prompt 版控、流程編排、模型切換、即時觀測、品質閘門與人審核一應俱全。團隊用同一套標準打造、治理、優化任何 Agent 流水線——旗艦應用已在大型主機現代化上跑出實證成效。
AWS AI-DLC Platform consolidates fragmented AI agent development into one platform: prompt versioning, workflow orchestration, model switching, live observability, quality gates and human-in-the-loop review—all built in. Teams build, govern and optimize any agent pipeline under one standard—its flagship already delivers proven results in mainframe modernization.
當 AI Agent 從實驗走向生產，缺的不是更多 Agent，而是一套讓 Agent 可被治理、觀測、複製、持續優化的平台。
As AI agents move from experiment to production, what's missing isn't more agents — it's a platform that makes them governable, observable, reproducible and continuously improvable.
不只是跑 Agent，而是把 Agent 當成可管理的軟體資產——這六項能力，任何流水線都能共用。
Not just running agents — operating them as managed software assets. Shared by every pipeline.
Monaco Editor 編輯 system prompt，支援版本歷史、Diff 比對、一鍵回滾。
Edit system prompts in Monaco with full history, diff comparison and one-click rollback.
React Flow 拖拉式 workflow 設計，支援並行步驟與版控，視覺化調整 pipeline。
Drag-and-drop workflow design in React Flow with parallel steps and versioning.
每個 Agent 可獨立選用 Claude Opus / Sonnet / Haiku，依階段難度平衡準確度與成本。
Pick Claude Opus / Sonnet / Haiku per agent — balance accuracy vs. cost by stage.
AG-UI SSE 把 pipeline 執行過程即時推送到 Dashboard，每一步看得見。
AG-UI SSE streams pipeline execution live to the dashboard — every step visible.
Agent Registry + 生命週期治理 + 稽核軌跡，註冊、發現與健康一覽無遺。
Agent registry, lifecycle governance and audit trail — at a glance.
透過 AgentCore Gateway 串接外部工具與資料源，標準化 Agent 的能力擴充。
Connect external tools and data via AgentCore Gateway — a standard interface.
大型主機累積數十年的業務邏輯正面臨「無人能完整講述」的傳承斷層。AI-DLC 用一條 Multi-Agent 流水線，把逆向工程、資料血緣、SQL 產出到測試驗證端到端跑通。
Decades of mainframe business logic face a knowledge gap. AI-DLC runs a multi-agent pipeline end-to-end — reverse engineering, data lineage, SQL generation, test validation.
同樣的平台能力可複製到任何需要多步推理 + 人審核的 AI 開發場景。
The same platform capabilities generalize to any AI workflow needing multi-step reasoning with human review.
從上傳原始碼、啟動流水線、即時串流執行，到品質閘門審核與產物輸出。
From uploading source, launching the pipeline and streaming execution, to gate review and artifact output.
AWS AI-DLC Dashboard 實機操作 · 約 1 分 52 秒AWS AI-DLC Dashboard walkthrough · ~1 min 52 sec
Serverless Agent Runtime、託管 Memory 與 Gateway，基礎設施全數以 AWS CDK 定義、可重現。
Serverless agent runtime, managed memory and gateway — fully defined and reproducible with AWS CDK.
全部基礎設施以 AWS CDK (TypeScript) 定義，6 個 Stack，idempotent 可重現部署。All infrastructure in AWS CDK (TypeScript) — 6 stacks, idempotent reproducible deploy.
於大型金融機構核心系統轉型 PoC 中，以人天為單位實測比較。
Measured in person-days during a core-system modernization PoC at a major financial institution.
* 數據來自實際客戶 PoC 實測（9 情境、單位人天）。客戶資訊已匿名化，實際成效因案例複雜度而異。* From actual customer PoC (9 scenarios, person-days). Customer anonymized; results vary by complexity.
從主機現代化到任何需要多步推理 + 人審核的場景，AWS AI-DLC 讓 AI 開發變成可重複、可審核、可累積的工程。
From mainframe modernization to any multi-step, human-reviewed workflow — AI-DLC makes AI development repeatable, auditable and cumulative.