We listened to customers as they refined their AI strategies in response to the rapid evolution of LLMs, Agentic AI and integration technologies such as the Model Context Protocol (MCP), and as we did so a few things stood out to us.
First and foremost, many of the newly available tools and technologies are not suited to the needs of the enterprise, particularly in highly regulated industries or major government agencies. Many of the new AI application builders and code generators – and the database platforms supporting them – do not adequately address enterprise requirements for high availability, data sovereignty, global deployment, security and compliance and the need in some cases to run on-premises or in self-managed cloud accounts. As one CIO from a large financial services firm put it to us recently: “We’ve got a couple of dozen AI generated applications end users really want to put into production, but first we’ve got to figure out how to deploy them on our own internal compliant infrastructure.”
Secondly, as compelling as it is to automate workflows with Agentic AI, or to generate new applications with tools like Claude Code, Replit, Cursor or Lovable, the biggest need is to work with existing databases and applications. While some of the newer Postgres-based cloud services work well with Agentic AI and AI app builders for brand new applications they cannot accommodate existing databases and applications without a costly migration – and perhaps to an environment that doesn’t meet the organization’s strict security and compliance requirements. Enterprise customers need AI tooling – including an MCP Server – that can operate against their existing databases.
Additionally we saw there was no dedicated Postgres vendor offering a fully featured and fully supported MCP Server that works with all your existing Postgres databases. Most of the available Postgres MCP Servers are tied to the vendor's own products, and in particular their cloud database offering.
And thirdly, developing new AI applications such as a chatbot running on top of an existing knowledge base, is overly complex with developers having to stitch together too many tools, APIs, Postgres extensions and data pipelines. We saw an opportunity to make it easier to develop AI applications without having to undertake a major exercise in tool sourcing and integration.
We are addressing each of these with the pgEdge Agentic AI Toolkit for Postgres.
We listened to customers as they refined their AI strategies in response to the rapid evolution of LLMs, Agentic AI and integration technologies such as the Model Context Protocol (MCP), and as we did so a few things stood out to us.
First and foremost, many of the newly available tools and technologies are not suited to the needs of the enterprise, particularly in highly regulated industries or major government agencies. Many of the new AI application builders and code generators – and the database platforms supporting them – do not adequately address enterprise requirements for high availability, data sovereignty, global deployment, security and compliance and the need in some cases to run on-premises or in self-managed cloud accounts. As one CIO from a large financial services firm put it to us recently: “We’ve got a couple of dozen AI generated applications end users really want to put into production, but first we’ve got to figure out how to deploy them on our own internal compliant infrastructure.”
Secondly, as compelling as it is to automate workflows with Agentic AI, or to generate new applications with tools like Claude Code, Replit, Cursor or Lovable, the biggest need is to work with existing databases and applications. While some of the newer Postgres-based cloud services work well with Agentic AI and AI app builders for brand new applications they cannot accommodate existing databases and applications without a costly migration – and perhaps to an environment that doesn’t meet the organization’s strict security and compliance requirements. Enterprise customers need AI tooling – including an MCP Server – that can operate against their existing databases.
Additionally we saw there was no dedicated Postgres vendor offering a fully featured and fully supported MCP Server that works with all your existing Postgres databases. Most of the available Postgres MCP Servers are tied to the vendor's own products, and in particular their cloud database offering.
And thirdly, developing new AI applications such as a chatbot running on top of an existing knowledge base, is overly complex with developers having to stitch together too many tools, APIs, Postgres extensions and data pipelines. We saw an opportunity to make it easier to develop AI applications without having to undertake a major exercise in tool sourcing and integration.
We are addressing each of these with the pgEdge Agentic AI Toolkit for Postgres.