AI Agents for Developers: Learning Hub

AI agents are changing how developers build software, automate workflows, and interact with AI. Whether you're learning the fundamentals, exploring agent architectures, or building production-ready systems, this hub brings together practical guides covering every stage of the journey.

Start with the basics, then explore how AI agents work, how to build them, and how to deploy them securely in real-world applications.

"On average, developers report that approximately 46% of the code they produce is fully generated by AI agents, 39% is written with AI assistance, and 27% is written entirely manually."

Source: Preliminary findings from the JetBrains Developer Ecosystem Survey 2026 • 15,000+ developers worldwide

Foundations

New to AI agents? Start here. These articles explain the core concepts, terminology, and differences between AI agents, LLMs, assistants, and other related technologies before you dive into implementation.

What Is Agentic AI?

Learn what agentic AI is and how it differs from traditional AI systems.

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What Are AI Agents?

Understand how AI agents work and where developers use them.

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What Are Autonomous AI Agents?

Learn how autonomous AI agents execute workflows within defined boundaries.

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AI Agents vs AI Assistants

Compare AI agents and AI assistants to choose the right approach.

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LLMs vs AI Agents

Compare LLMs and AI agents across memory, tools, and workflow execution.

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Agentic AI vs AI Agents

Learn how agentic AI and AI agents relate and where they differ.

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Agentic AI vs Generative AI

Compare agentic AI and generative AI across capabilities, workflows, use cases.

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Types of AI Agents

Explore the main AI agent types and when to use each.

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Single vs Multi-Agent Systems

Compare single-agent and multi-agent architectures for different workloads.

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Architecture and core concepts

Understand how modern AI agents work under the hood. Learn about planning, memory, orchestration, tool use, context management, and the building blocks that enable autonomous behavior.

AI Agent Orchestration

Learn how orchestration coordinates agents, tools, and workflow execution.

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Multi-Agent Systems

Learn how multiple AI agents coordinate to solve complex development workflows.

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AI Agent Architecture Explained

Explores AI agent architecture, including core components.

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Agentic Workflows Explained

Explains agentic workflows, how AI agents plan and execute multistep tasks.

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AI Agent Context Windows

Learn how to manage AI agent context windows, reduce context loss.

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What Is Model Context Protocol?

Learn what Model Context Protocol (MCP) is how it connects AI agents with tools

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AI for Engineering Teams

Coming soon

A new hub covering AI governance, observability, secure operations, and agentic workflows for engineering teams.

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.

Junie

The AI coding agent with deep IDE integration that plans before it writes, then codes and tests while you stay in flow.

JetBrains AI in IDEs

Set of AI-powered capabilities built into JetBrains IDEs for software developers. It is not a standalone product or service, but an IDE-native experience composed of AI features, LLMs, agents, and integrations.

AIR

Agentic Development Environment for engineering teams building products with AI.

AI for Teams and Organizations

An open system for agentic software development. Govern AI access across your engineering org, manage agents and models, and keep costs under control.

JetBrains Context

A repository intelligence layer for coding agents. It builds a semantic index of your codebase so agents retrieve what they need instead of exploring it file by file.

Central CLI

One CLI for every terminal agent. Claude Code, Codex, Gemini, and others plug into JetBrains AI and behave exactly as they do standalone. Access is granted centrally and instantly, with models, limits, and usage analytics governed in one place.