The Signal-Activated Agent Pattern: A reference architecture for proactive government AI

This article presents the Signal-Activated Agent Pattern, a reference architecture enabling proactive government AI that delivers role-specific insights and actions based on real-time data events and consumer context.

• Delivers personalized value dimensions to different roles from the same data event using consumer context stored in DynamoDB
• Two-layer architecture: notification layer for role-shaped insights and action layer for autonomous or supervised execution
• Hot-path Lambda filter resolves 80% of events deterministically; escalated events invoke Amazon Bedrock with full consumer context
• Capabilities integrated via Model Context Protocol (MCP) for pluggable integration with existing agency tools and systems
• Structural safety controls: stateless Bedrock invocations, pre-authorized capability binding, reversibility tiering, and emergency switch via Systems Manager
• Single-table DynamoDB design with three GSIs optimized for event source, role-based fan-out, and rate-limit enforcement
• Supports both official-initiated queries and system-initiated event triggers through the same consumer context and delivery router
• Voice-activated intelligence layer enables conversational persona building for continuous refinement of user needs

The pattern enables government agencies to reach more constituents, react faster with contextual information, and scale proactive AI without proportional headcount increases.