AWS Multi-Agent System for Social Lead Mining
AWS ML Blog · rss · 2026-07-15
An AWS blog post details how to build a multi-agent social intelligence system using Strands Agents + Amazon Bedrock AgentCore. The system automatically aggregates signals from social platforms, communities, and code repositories to generate actionable sales leads.
Use Case
Thrad.ai, an "AI ad infrastructure" company, aims to enable ads in chat interfaces and brand marketing within LLMs. Their sales team previously spent 30–45 minutes manually researching a single prospect across multiple sites. They are now automating this workflow with multi-agents.
Architecture
The system is divided into 4 specialized agents:
- Trend Research: Identifies trends and buying intent signals from Hacker News, YouTube, dev.to, ProductHunt, Reddit, and Stack Overflow.
- Search Specialist: Conducts supplementary searches using Wikipedia, GitHub, Lobste.rs, and Stack Overflow.
- Analysis: Scores "prospects × trends" on a scale of 0–100.
- Email Generation: Drafts personalized outreach emails.
Each agent utilizes its own tools and Pydantic output constraints to prevent formatting errors from cascading through the pipeline.
Orchestration Comparison
The author compares two Strands orchestration methods:
- Swarm: Agents dynamically transfer control via handofftoagent while sharing working memory.
- Graph: Follows a more rigid, explicitly defined workflow.
Scoring Logic
The Analysis agent applies a multi-factor weighting system:
- Topic match: 25%
- Time relevance: 20%
- Interaction potential: 20%
- Intent signals: 20%
- Data quality: 15%
Additionally, it provides bonus weight for developer tools with open-source/B2B characteristics. Time decay is also applied: signals within 24 hours carry the highest weight, decaying to 0.5x after 7 days.
Conclusion
The article essentially demonstrates that multi-agent systems are highly suitable for cross-source signal integration, lead mining, and automated outreach, providing a practical AWS implementation path and orchestration benchmarks.
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