Role-Based Programme
RB0604

JetBrains AI Assistant for Information Technology

Architect, Automate & Govern Advanced AI-Assisted Engineering Workflows

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced expertise in using JetBrains AI Assistant across application development, codebase analysis, troubleshooting, testing, maintenance, and IT engineering workflows.
  • Architect agent-assisted workflows for multi-file development, debugging, refactoring, test generation, technical documentation, and repetitive engineering activities.
  • Integrate project rules, reusable prompts, agent instructions, Skills, MCP, ACP, and external tools into controlled development environments.
  • Apply AI assistance to scripts, configuration, APIs, infrastructure-related code, version control, and operational support while maintaining technical validation.
  • Establish enterprise-ready controls for model selection, sensitive-code protection, human approvals, quality assurance, traceability, and responsible AI-assisted engineering.

Tools covered

JetBrains AI AssistantAI ChatInline Code CompletionNext Edit SuggestionsAI ActionsPrompt LibraryGenerate Unit TestsAI DiffSelf-Review with AIProject RulesCoding AgentsAgent InstructionsAgent SkillsModel Context Protocol (MCP)Agent Client Protocol (ACP)VCS IntegrationTerminal-Assisted WorkflowsThird-Party & Locally Hosted Models`.aiignore` & `.noai`

Who should attend

  • IT Professionals
  • Software Developers
  • Application Support Engineers
  • System Administrators
  • DevOps Engineers
  • Cloud Engineers
  • Platform Engineers
  • Site Reliability Engineers
  • Integration Engineers
  • Technical Support Engineers
  • Application Engineers
  • IT Automation Professionals
  • Solution Engineers
  • Technical Leads
  • Engineering Managers

Prerequisites & Participant Readiness

  • Good understanding of software-development or application-support workflows
  • Ability to read and modify code in at least one programming or scripting language
  • Familiarity with Git, repositories, branches, commits, and code-review practices
  • Basic understanding of APIs, application architecture, configuration, and debugging
  • Familiarity with terminal or command-line environments is recommended
  • Basic understanding of testing and software quality is beneficial
  • Access to a supported JetBrains IDE with appropriate AI Assistant functionality
  • No previous JetBrains AI Assistant course completion required

TOC Modules

Concepts
  • Understanding AI Assistant 2026.2 within JetBrains IDE engineering workflows
  • Understanding AI Chat, models, AI Actions, code completion, agents, and contextual assistance
  • Differentiating conversational assistance from agent-driven multi-step engineering
  • Understanding project, file, symbol, folder, image, and other context sources
  • Mapping AI assistance across development, maintenance, support, testing, and automation
Practical activities
  • Configuring an AI-assisted engineering workspace
  • Comparing Chat and Agent workflows for selected IT tasks
  • Mapping IT Activity → AI Capability → Human Validation → Technical Outcome

Scenarios

Application Change to Governed Production-Ready Delivery

Business Requirement → Codebase Analysis → Project Rules → Agent Plan → Multi-File Implementation → Unit Tests → AI Self-Review → Defect Correction → Regression → Peer Review → Deployment Readiness

Participants use JetBrains AI Assistant across the complete engineering lifecycle while enforcing quality, security, testing, and human-approval controls before a change is considered release-ready.

Production Incident to Automated Preventive Improvement

Application Failure → Logs / Stack Trace → AI-Assisted Root-Cause Analysis → Agent Investigation → Controlled Fix → Regression Tests → Automation / Configuration Improvement → Documentation → Project Rule Update → Governance Review

Participants investigate a production-style incident, correct the underlying problem, establish regression protection, improve the related engineering workflow, and capture reusable knowledge that reduces recurrence.

## Current Capability Reference

JetBrains **AI Assistant 2026.2** currently provides AI Chat as the main entry point for working with language models and coding agents. Users can supply files, folders, symbols, images, and other relevant context, and models can be provided through JetBrains AI, supported third-party providers, or locally hosted environments.

Current coding-agent integrations include **Junie, Claude Agent, Codex, and GitHub Copilot**, with additional external agents connectable through **Agent Client Protocol (ACP)**. JetBrains also supports agent instructions, selected agent Skills, and **MCP tools** for connecting external systems and data sources. Exact agent capabilities and restrictions vary by agent, so enterprise delivery should validate the selected configuration.

JetBrains AI Assistant currently supports **unit-test generation** using both selected code and surrounding context, with generated tests presented through **AI Diff** for review before acceptance. Custom prompts and modifications to built-in actions can be maintained through the **Prompt Library**.

For governed development, **Self-Review with AI** can analyse both uncommitted and committed changes using organisation-defined review guidelines. **Project Rules** can provide reusable project-specific instructions, while project settings support `.aiignore` restrictions; JetBrains also supports a root-level `.noai` file to disable AI Assistant for an entire project.

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