From autonomous agents to vibe coding, 2025 was the year generative AI stopped being theoretical and started doing real work—with a little fun along the way. Our readers gravitated toward features and tutorials that explored how to move AI into production software and reshape developer workflows, and to columnists who forced uncomfortable (and sometimes amusing) questions about the role of humans in the AI-driven workplace. Here’s a look back at some of InfoWorld’s most popular AI coverage this year.
The year agents took off
2025 may be remembered, among other things, as the year AI agents moved beyond research concepts and toy demos to drive real-world applications and platforms. Agents can now handle everyday software tasks, integrate into developer workflows, and are embedded into large-scale enterprise infrastructure. Some of the year’s most popular articles looked at how AI agents were being used in production:
- Agentic coding with Google Jules
Software developers are among AI’s most enthusiastic fans, and Google Jules is an agentic coding assistant with real heft. It fixes bugs, adds documentation, and integrates with your GitHub repos. - How LinkedIn built an agentic AI platform
The careers behemoth built an enterprise-scale agent AI deployment, using an agentic platform that leverages distributed application techniques. Here’s a candid look at the real architectural decisions and practical engineering patterns used for agentic systems at scale. - Multi-agent AI workflows: The next evolution of AI coding
Now multi-agent systems are emerging, with coordinated workflows capable of completing complex coding tasks. Agents are starting to interoperate in real development contexts by sharing state, governance, and human-in-the-loop control mechanisms. - How AI agents will transform the future of work
AI agents are already reengineering software development, business processes, and customer experiences. What’s next?
Multi-agent systems? New protocols make it possible
As autonomous agents are embedded in real workflows, the next challenge is getting them to talk to each other and the tools they depend on. This year, open standards like the Model Context Protocol moved from experimental specs to practical infrastructure, enabling agents to share context, invoke external services, and participate in coordinated multi-agent workflows across environments:
