Conclusion and next steps

In this tutorial, you've built a Next.js app with a Google ADK agent that uses Snowplow Signals to deliver personalized, context-aware responses based on live user behavior.

Here's what you set up:

• Snowplow Browser tracker capturing page views, page pings, and link clicks
• A Signals attribute group computing real-time session-level attributes
• A Signals service exposing those attributes via API
• A CopilotKit sidebar that passes the Snowplow session ID with every request
• A Google ADK agent that fetches and injects those attributes into its system prompt

Here are some next steps ideas for extending what you've built.

Interventions

Signals also includes interventions. These are push-based triggers that fire when a user crosses a behavioral threshold.

Rather than waiting for the user to open the chat, you can proactively provide context to your agent when something significant happens. For example, a user who has viewed pricing five times without converting. Combine this with CopilotKit's useCopilotChatSuggestions to surface contextual prompts in the sidebar.

Try exploring how you could use interventions within this application.

Richer attributes

The Basic Web attribute group template covers session-level behavior. For further personalization, try extending your attribute group with:

• Product affinity: count of views per product category to understand user interest
• Engagement score: a computed signal of session depth and intent
• Return visitor flag: whether this is a new or returning user
• Funnel stage: where the user is in a defined conversion journey

Generative UI

CopilotKit's useCopilotAction lets the agent render React components instead of plain text. Combine it with Signals attributes to build contextual UI. For example, a pricing comparison card that only renders for users Signals has flagged as high-intent enterprise browsers.

Multi-agent routing

Google ADK supports SequentialAgent, ParallelAgent, and LoopAgent for composing multi-step workflows. You can route users to different sub-agents based on their Signals profile. For example, a support specialist for confused new users, or a technical deep-dive agent for developers in the docs.

Multi-dimensional context

The injection happens in a plain Python callback, so you can pull context from as many sources as you need. Combine Signals real-time attributes with batch data from your warehouse. This could include attributes such as user profile data, CRM attributes, or product usage history.

Deploy to Vertex AI Agent Engine

The ADK integration supports deployment to Vertex AI Agent Engine, Google's managed runtime for ADK agents. Set GOOGLE_GENAI_USE_VERTEXAI=True, swap your API key for a service account, and deploy.

Other Signals tutorials

Check out these other Signals tutorials and solution accelerators for inspiration:

• Set up Signals for real-time calculation
• Implement real-time interventions using Signals
• Score prospects in real time using Signals and ML
• Use AWS BedRock to supplement Signals with persistent user context