Your prospects leave trails across multiple sources: a founder asks “What should I use for X?” in r/SaaS while their product launches on Hacker News. Stack Overflow questions spike. A GitHub repo crosses 2,400 stars. Each signal alone is noise, but correlated across sources, they reveal a prospect ready to buy. Multi-agent systems built with Strands Agents and Amazon Bedrock AgentCore can automate this social intelligence at scale.

Thrad.ai is building the advertising infrastructure for AI, introducing paid ads in LLMs. Their platform lets chat interfaces monetize through ads and lets brands advertise in them. They faced an especially signal-rich version of this problem. Tracking these patterns manually doesn’t scale, and generic outreach lacks the context that makes email worth opening. Thrad.ai’s sales team spent 30 to 45 minutes researching each lead across six sources before writing one outreach email.

A single AI agent can’t solve this: the signal diversity is too broad, the source APIs too varied, and the analysis too nuanced for one model to handle well. With multi-agent orchestration, you assign each source to a specialist agent, then fuse results through a dedicated analysis agent that spots cross-source patterns.