Conclusion and next steps

You've built an AI agent that combines real-time behavioral data from Snowplow Signals with persistent memory from AWS Bedrock AgentCore Memory. The agent can:

• Answer queries using custom tools (destination lookup, experience info, web search)
• Fetch behavioral attributes from Signals to understand what the user is doing right now
• Retrieve and store customer context across conversations using AgentCore Memory
• Deliver personalized responses that reflect both browsing behavior and historical preferences

This pattern - tools plus behavioral context plus persistent memory - is useful in any customer-facing agent, from support bots to shopping assistants to content recommenders.

If you connected the SEA Explorer demo app, you also have a working frontend that calls Bedrock with Signals and Memory integration, demonstrating the full end-to-end flow.

Clean up

Delete the AgentCore Memory resources created during this accelerator to avoid ongoing costs.

python

from bedrock_agentcore.memory import MemoryClient
from boto3.session import Session

boto_session = Session()
region = boto_session.region_name

memory_client = MemoryClient(region_name=region)
memory_client.gmcp_client.delete_memory(memoryId=memory_id)
print(f"Deleted memory resource: {memory_id}")

If you published a Signals service and attribute group, run the optional cleanup cell at the end of the notebook to remove them:

python

sp_signals.unpublish([travel_service, session_attributes_group])
sp_signals.delete([travel_service, session_attributes_group])

Next steps

• Deploy to production: Use AgentCore Runtime to deploy your agent as a scalable, serverless endpoint with built-in observability. Use AgentCore Gateway to expose your tools as shared MCP endpoints with JWT authentication.
• Add more Signals attributes: Define additional behavioral attributes based on your application's event data - for example, purchase history, content engagement patterns, or feature usage.
• Explore accelerators: The Build a personalized travel agent with Signals accelerator covers a simpler implementation using OpenAI and Signals without persistent memory.
• Learn more about Signals: Read the Signals documentation for advanced attribute definitions, batch engine configuration, and CDI integration.
• Learn more about AgentCore: Explore the AgentCore developer guide and code samples.