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