Add Strands agent for AgentCore Runtime (tool via Gateway MCP)

agent/agent.py

@@ -0,0 +1,55 @@
+"""
+Cloud agent (steps 3 + 7)  --  runs on Amazon Bedrock AgentCore Runtime.
+
+It receives an ALREADY de-identified prompt from Fusion (contains a token like
+CUST_000123, never a name), reasons with a Bedrock model, calls the RAG tool
+through AgentCore Gateway (MCP), and returns de-identified talking points.
+Fusion restores the real identity on the way back out -- not this agent.
+
+Framework: Strands.  Deploy target: AgentCore Runtime (see scripts/agentcore_setup.py).
+Pin versions in requirements.txt; SDK surfaces move quickly.
+"""
+import os
+from bedrock_agentcore.runtime import BedrockAgentCoreApp
+from strands import Agent
+from strands.models import BedrockModel
+from strands.tools.mcp import MCPClient
+from mcp.client.streamable_http import streamablehttp_client
+
+app = BedrockAgentCoreApp()
+
+GATEWAY_URL = os.environ["AGENTCORE_GATEWAY_URL"]     # set by agentcore_setup.py
+GATEWAY_TOKEN = os.environ.get("AGENTCORE_GATEWAY_TOKEN", "")
+MODEL_ID = os.environ.get("BEDROCK_MODEL_ID", "anthropic.claude-3-5-sonnet-20241022-v2:0")
+
+SYSTEM_PROMPT = (
+    "You are a financial-advisor assistant. You will be given a customer reference "
+    "that is an opaque TOKEN (e.g. CUST_000123). Treat it as an opaque identifier: "
+    "never invent a name, and always pass the token verbatim to tools. Use the "
+    "get_customer_activity_summary tool to fetch a de-identified evidence package, "
+    "then produce concise, numbered visit talking points based only on that evidence. "
+    "Do not include the raw token in your final talking points."
+)
+
+
+def _mcp_client():
+    headers = {"Authorization": f"Bearer {GATEWAY_TOKEN}"} if GATEWAY_TOKEN else {}
+    return MCPClient(lambda: streamablehttp_client(GATEWAY_URL, headers=headers))
+
+
+@app.entrypoint
+def invoke(payload):
+    """payload == {'prompt': '<de-identified prompt from Fusion>'}"""
+    prompt = payload.get("prompt", "")
+    with _mcp_client() as client:
+        agent = Agent(
+            model=BedrockModel(model_id=MODEL_ID),
+            system_prompt=SYSTEM_PROMPT,
+            tools=client.list_tools_sync(),
+        )
+        result = agent(prompt)
+    return {"result": str(result)}
+
+
+if __name__ == "__main__":
+    app.run()