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      Learning Path

      Automic Automation: AI-Powered Capabilities

      Courses
      4 Chapters
      to review
      20 Minutes
      estimated completion

      Introduction

      This learning path explores the transformative generative AI capabilities introduced in Automic Automation v26. You will learn how the integration of the Model Context Protocol (MCP) and specialized AI Assistants streamline complex IT operations. From automating incident enrichment in ServiceNow to using natural language for building Workflows and scripts, this path demonstrates how AI-driven orchestration reduces manual effort and accelerates time-to-resolution across the enterprise.

      Automic Automation v26: Orchestrating Autonomous Intelligence

      Get an overview of the foundational AI advancements in Automic Automation v26. This video introduces the Model Context Protocol (MCP) infrastructure, which provides a secure and standardized way to connect the automation engine with Large Language Models (LLMs). Discover how new components like AI Jobs and AI Connection objects empower you to build scalable, governed AI agents that transform manual decision-making into automated intelligence.

       

      AI-Augmented Workflow Creation

      Explore how generative AI simplifies the creation of automated processes. Follow a scenario where a developer uses the AI Assistant to build a multi-object data pipeline between SQL Server and BigQuery simply by describing the desired outcome in plain language. The video covers how the assistant generates a detailed work plan, suggests folder structures, creates required connection objects, and even writes Python scripts for data uploads—all while maintaining human-in-the-loop oversight.

       

      Automic Automation: AI-Powered Incident Resolution

      Learn how to use the new AI Job type to automate the analysis and enrichment of IT service tickets. This demo showcases a real-world scenario where Automic Automation extracts insights from ServiceNow incidents, performs context-aware similarity searches, and ranks historical resolutions to formulate remedial steps. See how AI-driven insights are automatically added as work notes to tickets, significantly reducing the workload for IT operations staff.

       

      Automic Automation: Accelerate Troubleshooting and Remediation with AI

      Discover how the Automic AI Assistant reduces Mean Time to Resolution (MTTR) through intuitive, natural language interaction. This video demonstrates how the AI Assistant helps operators diagnose failed jobs by analyzing logs, retrieving technical details, and executing remediation actions directly from a chat interface. Explore the "agentic" experience where the assistant can even be integrated into collaboration platforms like Slack or Teams to streamline troubleshooting.