To build a useful multi-agent AI system, model the agents after your existing human team. Create specialized agents for distinct roles like 'approvals,' 'document drafting,' or 'administration' to replicate and automate a proven workflow, rather than designing a monolithic, abstract AI.

Instead of building a single, monolithic AI agent that uses a vast, unstructured dataset, a more effective approach is to create multiple small, precise agents. Each agent is trained on a smaller, more controllable dataset specific to its task, which significantly reduces the risk of unpredictable interpretations and hallucinations.

Instead of a single, general AI model that can lose context during a complex task, Protoboost uses eight distinct agents trained on specific datasets (e.g., market analysis, user needs). This architectural choice ensures each step of the validation process is more accurate and trustworthy.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

When multiple AI agents work as an ensemble, they can collectively suppress hallucinations. By referencing a shared knowledge graph as ground truth, the group can form a consensus, effectively ignoring the inaccurate output from one member and improving overall reliability.

The path to robust AI applications isn't a single, all-powerful model. It's a system of specialized "sub-agents," each handling a narrow task like context retrieval or debugging. This architecture allows for using smaller, faster, fine-tuned models for each task, improving overall system performance and efficiency.

When building Spiral, a single large language model trying to both interview the user and write content failed due to "context rot." The solution was a multi-agent system where an "interviewer" agent hands off the full context to a separate "writer" agent, improving performance and reliability.

The popular concept of multiple specialized agents collaborating in a "gossip protocol" is a misunderstanding of what currently works. A more practical and successful pattern for multi-agent systems is a hierarchical structure where a single supervisor agent breaks down a task and orchestrates multiple sub-agents to complete it.

Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.