Multi-Agent Orchestration

A competitive analysis crew has three agents: a researcher that searches Google and Reddit via Scavio, an analyst that compares pricing from Amazon data, and a reporter that synthesizes findings into a brief. CrewAI orchestrates the handoffs, ensuring each agent's output feeds the next.

Multi-Agent Orchestration is relevant to Google, YouTube, Amazon, Reddit. Scavio provides a unified API to access data from all of these platforms.

Single AI agents hit limits when tasks require diverse expertise, parallel processing, or checks and balances. Multi-agent orchestration solves this by dividing work across specialized agents: a researcher agent gathers data, an analyst agent evaluates it, a writer agent produces output, and a reviewer agent checks quality. Frameworks like CrewAI, LangGraph, and AutoGen provide the infrastructure for defining agent roles, managing communication between agents, handling state, and coordinating task execution order. The orchestration layer decides which agent runs next based on the current state and results from previous steps. Search APIs play a critical role as shared data tools: a researcher agent might use Scavio to pull Google SERP data, an analyst agent queries Amazon pricing, and a content agent fetches YouTube transcripts -- all through the same API with different endpoints, keeping the data layer consistent across the multi-agent system.