Signal-activated generative AI: How agencies can reach more people and react faster

This article introduces the Signal-Activated Agent Pattern, an architectural approach that shifts government AI from reactive question-answering to proactive, contextually personalized decision support.

• Signal-activated systems continuously monitor agency data and deliver personalized insights to each official based on their role and priorities
• Same data event produces different value dimensions for different consumers: researchers need analytics, policymakers need decision readiness, field coordinators need operations data
• Consumer context stored in DynamoDB maintains identity, operational priorities, and signal history across three logical layers
• Built on AWS managed services: EventBridge for event ingestion, Lambda for filtering, Bedrock for reasoning, DynamoDB for profiles
• Hot-path Lambda filters events against per-user watch conditions before invoking Bedrock for contextual reasoning
• Digest runner aggregates signals on schedule to prevent noise while ensuring nothing falls through cracks

The Signal-Activated Agent Pattern enables government agencies to reach more people, respond faster, and deliver precisely the right value to each official at each decision point.