The week brought major announcements about multi-agent AI systems making real changes in how businesses operate around the world. On October 28, 2025, the Eclipse Foundation, one of the world's largest open source organizations, unveiled the Agent Definition Language (ADL) as part of their Eclipse LMOS platform. This new language lets people describe how AI agents should work without needing to know complicated computer code. Business experts can now work directly with AI engineers to create AI agents that follow company rules and goals exactly.

The ADL solves a big problem in the AI world. Before this, creating AI agents took a very long time and only computer experts could do it. The new language uses simple instructions that anyone can understand and update when business needs change. Companies can update how their AI agents work without having to rewrite computer code, which saves time and money. The Eclipse Foundation says this makes Eclipse LMOS different from other AI platforms that companies have to buy from big tech companies.

Developers building software got great news when LangGraph 1.0 released its first stable version in October 2025. This tool makes it much easier to create teams of AI agents that work together smoothly. Instead of one AI trying to do everything, multiple AI agents divide the work into smaller, easier pieces. Each agent becomes an expert at one specific job, and they all talk to each other to solve bigger problems. This approach lets AI systems handle more complicated work and make smarter choices.

In the United States, Android Studio launched new AI agent features for developers who create phone apps. These agents help programmers write better code faster by understanding what the developer wants and suggesting solutions. At the same time, Cursor 2.0, a popular coding tool, released an October update with smarter multi-agent systems that beat older single AI methods. These tools show how agentic AI is helping workers do their jobs better and faster across many industries.

The biggest surprise news came from OpenAI, which released a private test version of Aardvark, an AI agent that acts like a security researcher. This agent automatically finds computer security problems and can even apply fixes without human help. Security researchers spend months finding problems that Aardvark might find in days, which could protect millions of people from computer attacks.

In the medicine and drug industry, McKinsey, a major business consulting company, shared exciting findings about agentic AI. They discovered that 75 to 85 percent of all work in drug companies could be done better or completely automated by AI agents. For example, AI agents could run clinical trials (medical tests on people) much faster and better. The McKinsey report predicts that within five years, AI agents could make drug development 35 to 45 percent faster across all parts of the company, from science work to writing reports.

Baker Hughes, an oil and gas equipment company, explained how multiple agents working together can think much better than a single agent. When AI agents can talk to each other and share information, they make fewer mistakes and give better advice. The company created agents that help engineers fix models and plan their daily work. Baker Hughes says that having one main agent managing all the other agents, called an Orchestrator agent, helps organize complex work and combine different types of expertise.

Experts predict huge changes coming soon. 96 percent of companies worldwide plan to use more agentic AI within the next 12 months. The world market for agentic AI could reach $200 billion by 2034, with banks and money companies being the first big users. Even more exciting, Gartner, a famous research company, predicts that by 2028, 15 percent of business decisions made in big companies will happen automatically through AI agents.

With so much power coming to multi-agent systems, safety became a hot topic. Security experts say builders of AI agents must log every decision and action so companies can check and audit what agents do. Companies need clear rules about what agents can and cannot do, and humans must always be able to stop an agent instantly if something goes wrong. Different types of agents need different permission levels, like how a small spending agent can approve a cheap lunch but should ask a human before spending thousands on business travel.

This week showed that agentic AI and multi-agent systems moved from being future ideas into tools that thousands of companies use right now to work faster, smarter, and better in many different industries and countries.