An architecture where multiple AI agents collaborate, each with specialized roles, to accomplish complex tasks that no single agent could handle alone.
A multi-agent system distributes complex work across specialized agents. Rather than one general-purpose AI trying to do everything, you have focused agents that excel at specific tasks: one for coding, another for testing, another for design review, another for security auditing. They communicate, share context, and build on each other's work.
The advantages over single-agent approaches are significant. Specialization means each agent can be optimized for its specific task with tailored prompts, tools, and evaluation criteria. Parallelism means multiple agents can work simultaneously on different aspects of a project. Quality control improves because agents can review each other's work — a testing agent validates what a development agent produces.
At Agentik {OS}, our multi-agent system is the core product. Our 198+ agents are organized into six departments, each with its own orchestrator. A project flows through these departments like it would through a traditional company: Strategy defines the approach, Design creates the UI, Development builds it, QA tests it, Marketing promotes it, and Operations maintains it. The coordination is automated, the quality is consistent, and the speed is unmatched.
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