Treasure Data announced the availability of its custom and out-of-the-box AI Agents in the new AI Agents and Tools category of AWS Marketplace. Customers can now use AWS Marketplace to easily discover, buy, and deploy AI agents solutions, including Treasure Data's AI Agent Foundry using their AWS accounts, accelerating agent and agentic workflow development. Treasure Data's AI Agent Foundy, built on Amazon Bedrock, helps enterprises build, deploy, and orchestrate custom AI agents across their customer data ecosystem, enabling them to automate decision-making, accelerate personalization, and unlock real-time insights at scale.
Treasure Data's AI Agent foundry delivers essential capabilities including flexible agent frameworks, low-latency decisioning infrastructure, and no-code tools for marketers and data teams. This enables customers to activate AI-driven use cases in days while maintaining full control over data, outcomes, and compliance. With the availability of AI Agents and Tools in AWS Marketplace, customers can significantly accelerate their procurement process to drive AI innovation, reducing the time needed for vendor evaluations and complex negotiations. With centralized purchasing using AWS accounts, customers maintain visibility and control over licensing, payments, and access through AWS.
Treasure Data also offers the Model Context Protocol (MCP) Server, a new open standard that enables secure, structured communication between AI agents and enterprise data sources. With MCP, companies can connect their preferred AI assistants directly to Treasure Data, making it easy to query databases, explore tables, and analyze data using natural language, no SQL required. This transforms how teams interact with customer data by lowering the barrier to entry for exploration and analysis.
Anyone, regardless of technical expertise, can ask questions and get actionable insights from Treasure Data using natural language. With a secure and governed interface, the MCP Server empowers organizations to make faster, smarter decisions.


















