How-Tos & Practical Guides

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. ...

A year ago, your organization had a dozen AI agents. Today you have hundreds, maybe thousands. Every team moved fast, built their own setup, and now you have a sprawling estate of agents — each with different authentication schemes, different tool access, different logging configurations. Then the CISO asks: “Which agents are accessing customer PII?” That’s the problem Databricks addressed in a May 20 engineering blog post, and the solution they’re presenting is Unity Catalog extended to govern the entire agent stack — not just data, but models, tools, and the MCP servers that connect agents to enterprise systems. ...

Your AI agent has a ticket queue full of infrastructure requests. It has read access to your runbooks, write access to your deployment pipelines, and the ability to execute changes against live systems. It also reads Jira tickets, wiki pages, and Slack transcripts to decide what to do next. That combination — broad access plus natural-language reasoning from untrusted inputs — is the attack surface security teams need to be thinking about right now. ...

Google ADK Python 2.0.0 Goes GA — Multi-Agent Workflow Engine with Graph-Based Execution Google’s Agent Development Kit (ADK) for Python has reached general availability at version 2.0.0, and it’s a substantial architectural leap from the 1.x series. Released on May 19, 2026, ADK 2.0 introduces a Workflow Runtime that replaces the previous hierarchical agent executor with a graph-based execution engine — a shift that opens up significantly more complex and realistic multi-agent patterns. ...

How to Use Claude Code Background Sessions with /resume for Long-Running Agentic Workflows Claude Code 2.1.144 shipped on May 19, 2026 with one of the most-requested quality-of-life improvements for practitioners running long autonomous tasks: /resume now works for background sessions. If you’ve been frustrated by detached runs that disappear from your session picker, this update is for you. What’s New in 2.1.144 The headline changes in this release are directly aimed at making multi-session, long-running agentic workflows more practical: ...

OpenClaw v2026.5.19-beta.2 Released — SDK Cleanup, Node.js 22 Minimum, Docker Build Args OpenClaw shipped v2026.5.19-beta.2 on May 19, 2026, and this pre-release build brings several meaningful infrastructure changes that existing operators will want to review before updating. This is a beta release — not production-stable — so test carefully in a staging environment before deploying to production nodes. The Node.js Floor Moves Up The most impactful change for most users: Node.js 22.19 is now the minimum required version. If you’re running anything older than 22.19, OpenClaw will not start after this update. ...

If you’ve been paying $20 a month for Claude Design and burning through your weekly token allowance in under thirty minutes, there’s now a free, self-hosted alternative that runs the same multi-agent design workflow entirely on your own machine. It’s called Open-Design, it’s Apache 2.0 licensed, and it just crossed 40,000 GitHub stars weeks after launch. What Open-Design Does Claude Design’s core value proposition is multi-agent design automation: feed it a prompt or a reference image, and a coordinated team of AI agents handles research, layout, copy, asset generation, and code — producing design artifacts that would take a human hours. Open-Design replicates that workflow but routes tasks to CLI-based AI agents running locally instead of Anthropic’s proprietary backend. ...

Every AI agent you’ve ever used follows the same basic script: you give it context, it acts. You write the system prompt, attach the documents, describe your preferences — and then the agent tries to help based on what you’ve given it in that session. OpenHuman inverts that script entirely. Instead of waiting for you to provide context, it spends time reading you first — analyzing your existing digital footprint to build a behavioral model — and only then begins acting on your behalf. ...

Multi-agent systems have a hidden cost problem that rarely appears in the demos: every time one agent communicates with another, it has to convert its internal reasoning into text, send it, and wait for the receiving agent to convert that text back into actionable computation. It’s the AI equivalent of printing a document, mailing it, and having someone retype it on arrival. RecursiveMAS proposes a different approach — and has the academic benchmarks to back it up. ...

An AI agent that fails silently is one of the most expensive debugging problems you’ll face. It picks tools, branches, retries, rewrites its own plan — and without visibility into those decisions, you’re flying blind. That’s the premise behind Motus Tracing, and it’s a compelling one. Lithos AI released Motus Tracing in May 2026 as a fully open-source observability layer for AI agents. It’s framework-agnostic, requires zero code changes to your agent, and the entire tracing stack is free to use. Here’s how it works and how to set it up. ...