Multi-agent systems, like microservices, can be powerful. But most enterprises risk adding distributed complexity long before they have a problem worth distributing.

InfoWorld is a source of news, analysis, and commentary on technology trends, IT strategies, and business innovation. With a focus on enterprise technology and digital transformation, InfoWorld offers insights and guidance for IT decision-makers, software developers, and technology professionals. From  articles on cloud computing and cybersecurity to product reviews and industry trends, InfoWorld helps readers navigate the complexities of modern IT environments and make informed decisions to drive business success.

InfoWorld

Multi-agent AI systems are being over-adopted in the same way microservices were — applied broadly before teams have problems that actually warrant the complexity. Anthropic, OpenAI, Microsoft, and Google all advise starting with the simplest solution: a single optimized LLM call, then retrieval, then tools, then a single agent loop. Only add a second agent when you can clearly identify parallelizable tasks, context pollution, or specialization needs. Multi-agent architectures cost significantly more in tokens, observability, error handling, and maintenance. Most enterprise teams don't yet have problems worth decomposing across agents, and adding agents won't fix weak retrieval, vague tools, or poor documentation — it will amplify those problems.

Multi-agent is the new microservices