--- title: Accelerate database modernization with agentic AI in AWS... description: AWS DMS Schema Conversion now supports AI agents that orchestrate entire database migration workflows through natural language via the Model Context Protocol... canonical: https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Accelerate database modernization with agentic AI in AWS DMS Schema Conversion | daily.dev og:description: AWS DMS Schema Conversion now supports AI agents that orchestrate entire database migration workflows through natural language via the Model Context Protocol... og:url: https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir og:image: https://api.daily.dev/og/posts/0vwMUG6iR.png og:image:alt: Accelerate database modernization with agentic AI in AWS DMS Schema Conversion og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Accelerate database modernization with agentic AI in AWS DMS Schema Conversion **[AWS Database Blog](https://daily.dev/sources/aws-database-blog)** · 6 min read · 0 upvotes · 0 comments ## Summary AWS DMS Schema Conversion now supports AI agents that orchestrate entire database migration workflows through natural language via the Model Context Protocol (MCP). Instead of manually navigating the console, developers can describe their intent and the agent handles project creation, metadata browsing, schema conversion, assessment report generation, and result export. In internal testing, a 200-object schema conversion completed in under 15 minutes versus ~45 minutes manually. The feature works with MCP-compatible coding agents like Kiro CLI, Claude Code, Cursor, and Codex. Early customer pilots report 60–70% reduction in schema conversion cycle time, with the biggest gains on projects with 50+ objects. The agent complements the existing rule-based engine and generative AI-assisted conversion launched in December 2024. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What does the new AI agent feature in AWS DMS Schema Conversion actually do? It lets an AI agent orchestrate the entire AWS DMS Schema Conversion workflow through natural language, including creating migration projects, browsing source metadata, converting schemas, generating assessment reports, and exporting results. This differs from the December 2024 generative AI feature, which only generated target SQL for individual unconvertible objects rather than managing the full lifecycle. _daily.dev surfaces releases like this for engineers planning large-scale AWS database migrations._ ### Which AI coding agents work with AWS DMS Schema Conversion's new agentic workflow? Any AI coding agent that supports the Model Context Protocol (MCP) with the AWS MCP Server configured, such as Kiro CLI, Claude Code, Cursor, or Codex. The agent loads a dedicated dms-schema-conversion skill from paths like .kiro/skills/, .claude/skills/, .cursor/skills/, or .agents/skills/ to translate natural language intent into DMS SC API calls. _developers comparing MCP-compatible coding agents for infrastructure tasks can track updates like this on daily.dev._ ### How much faster is agent-driven schema conversion compared to manual DMS console operation? A single agent-driven session completed a 200-object schema conversion in under 15 minutes, versus approximately 45 minutes of manual console navigation and API calls for the same scope. Early customer pilots reported 60-70% reductions in schema conversion cycle time overall, with the largest gains on projects containing 50 or more objects. _teams estimating migration timelines can follow performance data like this on daily.dev._ ## Similar posts on daily.dev - [AWS DMS Schema Conversion now supports AI agent automation](https://daily.dev/posts/aws-dms-schema-conversion-now-supports-ai-agent-automation-eiphjx8gv) · AWS · 0 upvotes · 0 comments - [SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS](https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh) · AWS Database Blog · 0 upvotes · 0 comments - [AWS DMS Schema Conversion now supports offline SQL Server conversion](https://daily.dev/posts/aws-dms-schema-conversion-now-supports-offline-sql-server-conversion-pwldpd7vk) · AWS · 0 upvotes · 0 comments - [DBmaestro MCP Server Puts Natural Language in Control of Database Pipelines](https://daily.dev/posts/dbmaestro-mcp-server-puts-natural-language-in-control-of-database-pipelines-tx3aumpiw) · InfoQ · 0 upvotes · 0 comments - [Bridging the Gap - How We Made AI Agents 10x Developers in Our Organization](https://daily.dev/posts/bridging-the-gap---how-we-made-ai-agents-10x-developers-in-our-organization-9xldqiaaq) · DEV · 4 upvotes · 0 comments --- Tags: [#database](https://daily.dev/tags/database), [#postgresql](https://daily.dev/tags/postgresql), [#mcp](https://daily.dev/tags/mcp), [#agentic-ai](https://daily.dev/tags/agentic-ai) [View this post on daily.dev](https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Accelerate database modernization with agentic AI in AWS DMS Schema Conversion","url":"https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir"},"datePublished":"2026-07-09T15:37:53.888Z","dateModified":"2026-09-13T20:28:14.240Z","description":"AWS DMS Schema Conversion now supports AI agents that orchestrate entire database migration workflows through natural language via the Model Context Protocol...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/eaf7a9ac84da14f0e9323b085f87c0e8?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/eaf7a9ac84da14f0e9323b085f87c0e8?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"AWS Database Blog","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"AWS Database Blog","logo":"https://media.daily.dev/image/upload/s--En-avd2a--/f_auto,q_auto/v1780213446/logos/aws-database-blog?_a=BAMAMiWQ0","url":"https://daily.dev/sources/aws-database-blog"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"database,postgresql,mcp,agentic-ai","timeRequired":"PT6M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"AWS Database Blog","item":"https://daily.dev/sources/aws-database-blog"},{"@type":"ListItem","position":3,"name":"Accelerate database modernization with agentic AI in AWS DMS Schema Conversion"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir#faq","mainEntity":[{"@type":"Question","name":"What does the new AI agent feature in AWS DMS Schema Conversion actually do?","acceptedAnswer":{"@type":"Answer","text":"It lets an AI agent orchestrate the entire AWS DMS Schema Conversion workflow through natural language, including creating migration projects, browsing source metadata, converting schemas, generating assessment reports, and exporting results. 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