Tag: agentic AI

  • DBmaestro Introduces Agentic Database DevOps with MCP Server

    Transforming Natural Language into Governed Platform Workflows

    Orlando, FL, April 7, 2026 – DBmaestro, the leading database DevSecOps platform, today announced the launch of its Model Context Protocol (MCP) server, making DBmaestro the first database DevOps platform purpose-built for agentic AI workflows.

    The DBmaestro MCP server exposes the company’s industry-leading database release automation, source control, CI/CD orchestration, and compliance capabilities to AI agents and enterprise copilots. Teams can now use natural language to trigger real DBmaestro workflows – automating project creation, multi-environment deployments, package management, and release orchestration – while preserving enterprise governance, role-based access control, and full auditability.

    “DBmaestro MCP turns our enterprise-grade database DevOps platform into an agentic operational layer for AI,” said Gil Nizri, CEO of DBmaestro. “DBAs and DevOps engineers can now interact in natural language to accelerate repetitive tasks, while AI becomes the interface to deterministic, governed workflows. This is not replacing database expertise – it’s amplifying it with enterprise-grade control.”

    Key capabilities include:

    • Agentic Database DevOps: AI agents execute complex database operations across dev, QA, and production environments
    • Natural Language Workflows: Prompts such as “Create an MsSQL release pipeline with Dev/QA/Prod environments, and update Dev and QA to the latest version” become real platform execution
    • Enterprise Governance: Role-based permissions, compliance tracking, and full audit trails remain intact
    • Deterministic Reliability: AI accelerates setup and self-service while engineers retain control over critical paths

    As IBM’s strategic OEM partner for database release automation, DBmaestro MCP extends this leadership into the agentic era, enabling organizations to scale database delivery while maintaining the security and compliance standards enterprises demand.

    “Every enterprise adopting AI agents needs secure, governed access to their core platforms,” said Yaniv Yehuda, DBmaestro Founder & CPO. “DBmaestro’s MCP implementation shows how database DevOps can evolve from manual processes to agent-driven automation without sacrificing control.”

    The DBmaestro MCP server is available immediately to all customers and partners. For more information, visit https://www.dbmaestro.com/ai/dbmaestro-mcp-server  

    About DBmaestro

    DBmaestro is a leading provider of Agent-Governed database DevOps and release automation, enabling CI/CD for databases with controlled, auditable change management. The platform extends DevOps to the database layer with built-in compliance, observability, and governed AI execution, including MCP-based workflows for natural-language operations and intelligent troubleshooting under enterprise control. As IBM’s strategic OEM partner, DBmaestro powers database release automation in some of the world’s most complex and regulated environments and augments DevOps, automation, and governance solutions. Learn more at www.dbmaestro.com. 

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    Media contact
    Ben Gross
    +972-50-8452086
    beng@dbmaestro.com

  • Giving AI Agents the Keys to Real Infrastructure

    Giving AI Agents the Keys to Real Infrastructure

    AI coding assistants can generate pull requests faster than most teams can review them, and that mismatch is creating a new kind of bottleneck across engineering organizations. The volume of AI-generated code is growing rapidly, but without a reliable way to validate that code against real production environments, teams are left choosing between slowing down to manually review everything or accepting the risk of pushing untested changes forward.

    Alan Shimel speaks with Sumeet Vaidya, CEO and co-founder of Crafting.dev, about the emerging concept of closed-loop autonomous development. The idea is straightforward: rather than treating AI agents as tools that hand off code for humans to verify, give those agents the ability to test their own output against live dependencies and real infrastructure before a human ever needs to get involved.

    The conversation explores what it takes to make that work in practice. Traditional sandboxing approaches struggle to replicate the complexity of production environments, where services interact with databases, APIs and other systems in ways that simplified test setups cannot capture. Vaidya argues that the next step for AI-assisted development is not better code generation but better code validation, ensuring that what gets produced actually behaves correctly in context.

    For DevOps and platform engineering teams, the implications are significant. If AI agents can reliably validate their own work, the role of human engineers shifts from gatekeeping individual pull requests to overseeing outcomes and setting guardrails. That changes how teams think about automation, testing pipelines and the division of labor between humans and machines in modern software delivery.