Multi-agent social intelligence with Strands Agents and Amazon Bedrock
AWS Machine Learning Amit Deol
Thrad.ai built a multi-agent system using Strands Agents and Amazon Bedrock to automate lead research across six social platforms (Hacker News, Reddit, Stack Overflow, GitHub, dev.to, ProductHunt), reducing manual research time from 30-45 minutes per lead to an automated pipeline. The system deployed two orchestration patterns—Swarm and Graph—with Graph achieving 32-second average latency and $0.06 cost per prospect versus Swarm's 45 seconds and $0.08 cost, though Swarm produced higher email quality (8.2 vs 7.6 on human rating). Organizations can now automatically discover buying signals across multiple sources, score prospects 0-100 using weighted criteria and temporal decay, and generate personalized outreach at scale.
Why it matters
This post shows how Thrad.ai deployed a multi-agent system with Strands Agents and Amazon Bedrock AgentCore that automates the pipeline from prospect discovery through personalized email generation. The post compares two orchestration patterns (Swarm and Graph) with head-to-head benchmarks on latency, cost, and email quality. You’ll also learn how the system scores prospects using weighted criteria, intent classification, and temporal decay, plus governance controls for production deployment.
