AI for Engineering Teams:
Learning Hub

To help organizations build the oversight and infrastructure needed to scale AI across their engineering teams, we created a regularly updated learning hub offering practical guidance on AI governance, agent observability and management, cost control and ROI measurement, and secure AI operations.

AI governance

How you approach AI governance today will make all the difference for the future. Introduce AI tools safely, create policies, manage shadow AI, and establish frameworks that give teams flexibility while maintaining organizational guardrails.

AI Governance for Engineering Teams: A Complete Guide

Discover how to govern AI tools, agents, and usage across engineering teams.

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Shadow AI in Engineering Teams: Risks, Costs, and How to Regain Control

Understand why shadow AI emerges, the risks it creates, and how engineering organizations can regain visibility and control.

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How to Safely Introduce AI Tools Into Software Engineering Workflows

Learn how to evaluate, introduce, and scale AI tools without creating unnecessary risk or disruption.

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Enterprise AI Governance Framework for Engineering Teams

Build a governance framework for managing AI adoption across your organization.

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How to Create an AI Usage Policy for Engineering Teams

Create clear guidelines for how developers and engineering teams can use AI responsibly.

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How to Standardize Agent Rules, Skills, and MCPs Across Engineering Teams

Create shared standards for agent instructions, reusable skills, and tool connections while giving teams room to adapt.

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Spec-Driven Development Without the Specification Bottleneck

Define requirements and acceptance criteria that keep agents on track while keeping specification work manageable.

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Agent observability

Understanding what AI agents are doing across your engineering organization is a must. Know how to track agent activity, measure workflows, and build the visibility teams need as agentic development scales with these guides.

AI Agent Observability for Software Engineering Teams: A Complete Guide

Learn how to monitor AI agent activity, behavior, and outcomes across engineering workflows.

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What Is Agent-First Development for Engineering Teams?

Explore agent-first development and what changes when teams design engineering workflows around AI agents.

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Event-Triggered Agent Workflows: From Ticket, Alert, and CI Failure to PR

Understand how developers and AI agents coordinate work across team-based engineering workflows.

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Cost management and ROI

When agentic AI enters the SDLC, costs and usage can quickly spiral out of control. Reduce unnecessary tool proliferation, manage spend, and give teams access to AI without losing oversight.

AI Cost Attribution for Engineering Teams: Track Spend by Agent and Team

Learn how to attribute AI costs to individual agents and teams so you can understand where your engineering team’s AI spend is going.

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How to Reduce AI Tool Sprawl and Vendor Complexity in Engineering Teams

Consolidate AI tooling, reduce overlapping services, and simplify how engineering teams access models and agents.

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AI Coding Productivity vs. Software Delivery Performance

Understand why faster code generation does not always mean faster delivery, and learn which metrics reveal the difference.

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AI security

AI agents can only thrive for your needs if appropriate guardrails are in place. Here’s how to control agent access, isolate execution, and reduce the security risks that come with increasingly autonomous workflows.

AI Agent Sandboxing: How to Isolate Agent Tasks and Limit Blast Radius When Things Go Wrong

Understand how sandboxing isolates agent execution and limits the impact of mistakes or compromised workflows.

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Secure Background Agent Execution: Running Agents Off Developer Laptops

Move agent tasks into managed environments and establish the permissions, execution limits, and monitoring they need.

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Use AI agents in your development workflow

Explore JetBrains AI solutions that help you build, use, and scale AI agents across the software development lifecycle.

Air Gateway

Run terminal agents through one JetBrains account, with access, models, and usage governed centrally.

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Junie

Plan, code, debug, and automate tasks with a coding agent that works across your terminal, IDE, GitHub, and GitLab.

Try Junie

Air in IDEs

Orchestrate AI agents and verify their output directly in JetBrains IDEs, with full control over how changes are reviewed.

Try Air in IDEs

Air Teams

Automate software delivery workflows, coordinate agentic work across teams, identify bottlenecks affecting delivery.

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Air Governance

Govern AI usage, models, policies, and costs across your organization, including JetBrains and third-party tools.

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Air Context

Give agents shared organizational memory and context that carries across tools, workflows, and execution environments.

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JetBrains Air

An open system of AI products for developers, teams, and organizations – from coding with agents to automating workflows and governing AI at scale.