JetBrains Koog Hits 1.0: Building Your First Production AI Agent in Kotlin

If you’ve ever tried to build a reliable AI agent on the JVM and ended up with a spaghetti mess of LLM calls, retry logic, and unexplainable failures — JetBrains just dropped your solution. At KotlinConf'26 in Munich, Koog reached stable 1.0, and it came with a real-world production story: Mercedes-Benz is using it to power vehicle maintenance scheduling agents at dealers worldwide.

That’s not a “we tested it in staging” headline. That’s enterprise. That’s production. Let’s unpack what Koog actually is, how it works, and how you’d get started building with it today.

What Is Koog?

Koog is JetBrains’ open-source, JVM-native AI agent framework. It’s designed from the ground up to work idiomatically in Kotlin — not as a port or thin wrapper around Python tooling, but as a first-class Kotlin framework that embraces the language’s type system and multiplatform capabilities.

The core philosophy is reliability over magic. Rather than throwing a vague prompt at an LLM and hoping for the best, Koog models agents as explicit, testable graphs with:

  • Type-safe workflow DSLs — you define agent behavior in structured Kotlin code, not ad-hoc prompt templates
  • Persistence and checkpointing — long-running agents can pause, recover, and resume without losing state
  • Strategies, Tools, and Models abstractions — cleanly separated concerns that let you reason about each layer independently
  • Tracing and observability — built-in support for watching what your agent actually did and why

It integrates with Spring Boot, Ktor, popular observability tools, and virtually every major LLM provider: OpenAI, Anthropic, Google, Ollama, and more.

The Mercedes-Benz Production Story

Here’s why 1.0 matters: the Koog team didn’t just ship a framework — they validated it in one of the world’s most demanding enterprise environments.

Mercedes-Benz.io adopted Koog for their Aftersales division, specifically for dealer vehicle service scheduling agents. These agents handle real customer requests, verify vehicle and dealer data, check availability, apply business rules, and submit bookings. The failure mode isn’t “slightly wrong output” — it’s a missed service appointment on a physical car.

Their approach decomposed the workflow into discrete, verifiable steps:

  1. Understand the request — parse user intent with the LLM operating inside strict guardrails
  2. Fetch verified data — pull confirmed vehicle history, dealer calendars, availability windows
  3. Run validations — apply business rules and eligibility checks before proceeding
  4. Compose summaries — generate human-readable confirmation of what’s about to happen
  5. Request confirmation — require explicit acknowledgment before committing
  6. Submit booking — execute the transaction only after all prior steps pass

Each step is modeled as a Koog Tool or Strategy. The LLM makes decisions within this explicit structure rather than in free-form space. The result: traceable, testable, auditable agents that can be debugged like normal code.

Getting Started with Koog

Koog is publicly available on GitHub with full documentation and quickstarts at jetbrains.com/koog.

To add Koog to a Gradle project, you’d add the dependency via JetBrains’ repository. From there, the core abstractions are:

  • Agent — the top-level entity that coordinates Tools and Models
  • Tool — a discrete, typed function the agent can call (API call, data fetch, validator)
  • Strategy — higher-level reasoning and routing logic
  • Model — the LLM backend (swappable without changing agent logic)

The type-safe DSL means your IDE catches errors at compile time — no more discovering that your agent silently ignored a validation step because of a typo in a string.

⚠️ How-To Accuracy Note: The specific DSL syntax for Koog 1.0 is evolving. Always refer to the official Koog documentation and the quickstart examples on GitHub for the exact API. The concepts described here are accurate to the stable 1.0 release announced at KotlinConf'26 — but always verify against the current docs before shipping production code.

Why This Matters for JVM Teams

For years, Python has dominated the AI agent tooling landscape. LangChain, CrewAI, AutoGen — they’re all Python-first. JVM teams building on Kotlin or Java have had to choose between bolting on Python microservices or wrapping Python SDKs with questionable type safety.

Koog changes that calculus. You get:

  • Native Kotlin Multiplatform support — agents can target Android, JVM backends, and eventually WebAssembly
  • IntelliJ/Android Studio first-class support — refactoring, debugging, and code completion that just works
  • Spring Boot/Ktor integration — drop agents into existing enterprise stacks without runtime friction
  • On-device AI patterns — for Android teams, on-device model inference is now part of the agent story

And as the Mercedes-Benz example shows, Koog is specifically built for the enterprise requirements that often break other frameworks: auditability, predictability, integration with existing data systems, and the ability to explain to a compliance officer exactly what the agent did.

Who Should Be Looking at Koog Right Now

  • Android/mobile teams exploring on-device AI that needs to interact with backend agents
  • Spring Boot shops wanting to add agentic workflows to existing services
  • Enterprise teams that have been burned by “black box” LLM agents and need structured, testable alternatives
  • JVM devs who want to stay in their ecosystem rather than shimming into Python

Koog isn’t the only enterprise agent framework. But it’s the first one that treats Kotlin as a first-class citizen, ships with a production enterprise case study, and comes with JetBrains’ IDE support baked in. That combination is worth taking seriously.

Sources

  1. KotlinConf'26 Keynote Highlights — JetBrains Blog
  2. Koog Official Product Page — JetBrains
  3. Mercedes-Benz.io: The Guardrails Your LLM Needs — Reliable Agent-Based Systems

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260521-2000

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