The next stage of this evolution is multi-agent software engineering, where specialized AI agents collaborate to solve complex business workflows instead of relying on a single monolithic model. A planner may decompose work, researcher agents retrieve enterprise knowledge, coding agents generate implementations, reviewer agents validate outputs, and execution agents perform approved actions.
The next stage of this evolution is multi-agent software engineering, where specialized AI agents collaborate to solve complex business workflows instead of relying on a single monolithic model.
The primary challenge is not model intelligence but system reliability.
A planner may decompose work, researcher agents retrieve enterprise knowledge, coding agents generate implementations, reviewer agents validate outputs, and execution agents perform approved actions.
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The next stage of this evolution is multi-agent software engineering, where specialized AI agents collaborate to solve complex business workflows instead of relying on a single monolithic model. A planner may decompose work, researcher agents retrieve enterprise knowledge, coding agents generate implementations, reviewer agents validate outputs, and execution agents perform approved actions. Although this architecture appears attractive, production deployments reveal that coordinating multiple agents resembles building a distributed system far more than writing prompt chains.
The primary challenge is not model intelligence but system reliability.
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