--- title: SQL Server to Aurora PostgreSQL conversion with AI... description: AI agents connected via an AWS MCP Server and a dms-schema-conversion skill can orchestrate SQL Server to Aurora PostgreSQL schema conversion through natural... canonical: https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS | daily.dev og:description: AI agents connected via an AWS MCP Server and a dms-schema-conversion skill can orchestrate SQL Server to Aurora PostgreSQL schema conversion through natural... og:url: https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh og:image: https://api.daily.dev/og/posts/24WUIviYh.png og:image:alt: SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS 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. # SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS **[AWS Database Blog](https://daily.dev/sources/aws-database-blog)** · 17 min read · 0 upvotes · 0 comments ## Summary AI agents connected via an AWS MCP Server and a dms-schema-conversion skill can orchestrate SQL Server to Aurora PostgreSQL schema conversion through natural language using AWS DMS Schema Conversion. The post explains the four-step conversion pipeline (parse, resolve, transform, generate), action item severity levels from INFO to CRITICAL, and decision frameworks for common incompatibilities like full-text search, error handling functions (XACT_STATE, ERROR_*), and CLR assemblies. It stresses that AI-generated and rule-based output alike only guarantees syntactic validity, not semantic equivalence, so functional regression testing against source behavior is required before production use. Operational guidance covers DMS quotas, throughput tuning, concurrency, and cost considerations for running conversions at scale. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I resolve CRITICAL action items from AWS DMS Schema Conversion when migrating SQL Server to Aurora PostgreSQL? CRITICAL action items mean the conversion engine could not convert a construct and require investigation into whether the cause is a target-engine incompatibility, unresolved dependency, unsupported syntax, or a service conversion gap such as hierarchyid, full-text search, or CLR assemblies. Apply manual or architectural remediation, and open an AWS Support case if you believe the construct should be supported. _daily.dev surfaces practical guidance for engineers untangling database migration edge cases like this._ ### Does AWS DMS Schema Conversion validate that AI-generated PL/pgSQL code is semantically correct? No, it only validates that AI-generated output parses as syntactically valid PL/pgSQL, not that it produces the same results as the original SQL Server code. Successfully converted AI-assisted objects get an INFO action item flagging generative AI provenance and requiring customer review, so functional testing against known inputs is still necessary before production use. _Teams validating AI-generated database code can track best practices like this on daily.dev._ ### How should I handle SQL Server XACT_STATE checks when migrating stored procedures to PostgreSQL? XACT_STATE has no direct PostgreSQL equivalent, so simply removing the unsupported function call is not sufficient since SQL Server and PostgreSQL expose different transaction and exception semantics. The surrounding branch must be reviewed and intentionally redesigned, preserving required warning, logging, and return behavior, and capturing error details inside a PL/pgSQL EXCEPTION handler. _daily.dev helps engineers dig into cross-database migration gotchas like transaction semantics differences._ ## 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 - [AWS DMS Schema Conversion adds SAP \(Sybase\) ASE to PostgreSQL support with generative AI](https://daily.dev/posts/aws-dms-schema-conversion-adds-sap-sybase-ase-to-postgresql-support-with-generative-ai-6kkcht1lf) · AWS · 1 upvotes · 0 comments - [Migrating SQL Server to Aurora PostgreSQL: Solving the Real Challenges of Cloud Database Modernization](https://daily.dev/posts/migrating-sql-server-to-aurora-postgresql-solving-the-real-challenges-of-cloud-database-modernizati-fvnjvvn7l) · Security Boulevard · 0 upvotes · 0 comments - [Accelerate database modernization with agentic AI in AWS DMS Schema Conversion](https://daily.dev/posts/accelerate-database-modernization-with-agentic-ai-in-aws-dms-schema-conversion-0vwmug6ir) · AWS Database Blog · 0 upvotes · 0 comments - [How to migrate from Oracle to Amazon Aurora PostgreSQL using AWS CloudFormation \(Part 1\)](https://daily.dev/posts/how-to-migrate-from-oracle-to-amazon-aurora-postgresql-using-aws-cloudformation-part-1--rb6x7noud) · AWS Database Blog · 1 upvotes · 1 comments --- Tags: [#aws](https://daily.dev/tags/aws), [#database](https://daily.dev/tags/database), [#mcp](https://daily.dev/tags/mcp), [#microsoft-sql-server](https://daily.dev/tags/microsoft-sql-server) [View this post on daily.dev](https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh) ```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":"SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS","url":"https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh"},"datePublished":"2026-09-01T15:24:11.694Z","dateModified":"2026-09-01T15:24:38.365Z","description":"AI agents connected via an AWS MCP Server and a dms-schema-conversion skill can orchestrate SQL Server to Aurora PostgreSQL schema conversion through natural...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6dba06a84adea861e8b53beb72db2ca8?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6dba06a84adea861e8b53beb72db2ca8?_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/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"aws,database,mcp,microsoft-sql-server","timeRequired":"PT17M"} {"@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":"SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/sql-server-to-aurora-postgresql-conversion-with-ai-agents-for-aws-dms-24wuiviyh#faq","mainEntity":[{"@type":"Question","name":"How do I resolve CRITICAL action items from AWS DMS Schema Conversion when migrating SQL Server to Aurora PostgreSQL?","acceptedAnswer":{"@type":"Answer","text":"CRITICAL action items mean the conversion engine could not convert a construct and require investigation into whether the cause is a target-engine incompatibility, unresolved dependency, unsupported syntax, or a service conversion gap such as hierarchyid, full-text search, or CLR assemblies. 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