snowplow · jborlase-snowplow · Jul 29, 2026 · Jul 30, 2026 · Jul 30, 2026 · Jul 30, 2026
diff --git a/tutorials/signals-google-adk-agent/adk-architecture.png b/tutorials/signals-google-adk-agent/adk-architecture.png
diff --git a/tutorials/signals-google-adk-agent/build-agent.md b/tutorials/signals-google-adk-agent/build-agent.md
diff --git a/tutorials/signals-google-adk-agent/conclusion.md b/tutorials/signals-google-adk-agent/conclusion.md
@@ -2,9 +2,9 @@
 title: "Conclusion and next steps"
 sidebar_label: "Conclusion"
 position: 6
-description: "Run the full stack, debug common issues, and explore extensions like interventions, richer attributes, generative UI, multi-agent routing, and Vertex AI deployment."
-keywords: ["debugging", "interventions", "generative UI", "multi-agent", "Vertex AI Agent Engine"]
-date: "2026-04-17"
+description: "Review what you built with Signals, Google ADK, and CopilotKit, and explore extensions like interventions, richer attributes, generative UI, multi-agent routing, and Vertex AI deployment."
+keywords: ["Signals", "interventions", "agentic context", "generative UI", "multi-agent", "Vertex AI Agent Engine"]
+date: "2026-08-04"
 ---
 
 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.
@@ -14,42 +14,20 @@ 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 Signals agentic context buffering the session's recent events as an LLM-ready narrative
 * 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
+* A Google ADK agent whose `before_model_callback` fetches both kinds of context and injects them into its system instruction each turn
 
-Here are some next steps ideas for extending what you've built.
+## Next steps
 
-## Interventions
+Here are some ways to extend what you've built:
 
-Signals also includes [interventions](/docs/signals/concepts/#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](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview), Google's managed runtime for ADK agents. Set `GOOGLE_GENAI_USE_VERTEXAI=True`, swap your API key for a service account, and deploy.
+* **Interventions**: Signals also includes [interventions](/docs/signals/concepts/#interventions), 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, such as a user who has viewed pricing five times without converting. Combine this with CopilotKit's `useCopilotChatSuggestions` to surface contextual prompts in the sidebar.
+* **Richer attributes**: the Basic Web template covers session-level behavior. Extend your attribute group with product affinity (views per category), an engagement score, a return visitor flag, or a funnel stage.
+* **Generative UI**: CopilotKit's `useCopilotAction` lets the agent render React components instead of plain text. Combine it with Signals attributes to build contextual UI, such as a pricing comparison card that renders only 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, so you can route users to different sub-agents based on their Signals profile.
+* **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, 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](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview), 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
 
@@ -58,4 +36,4 @@ Check out these other Signals tutorials and solution accelerators for inspiratio
 * [Set up Signals for real-time calculation](/tutorials/signals-quickstart/start)
 * [Implement real-time interventions using Signals](/tutorials/signals-interventions/start)
 * [Score prospects in real time using Signals and ML](/tutorials/signals-ml-prospect-scoring/intro)
-* [Use AWS BedRock to supplement Signals with persistent user context](/tutorials/signals-agentic-accelerator/intro)
+* [Build a Signals-powered AI agent with AWS Bedrock AgentCore](/tutorials/signals-agentic-accelerator/intro)