Market surveillance agent with LangGraph and Strands on AgentCore

<p>As artificial intelligence applications evolve from simple chatbots to sophisticated autonomous systems, organizations face new challenges in orchestrating complex multi-agent workflows that can handle real-world production scenarios. Traditional single-agent approaches often fall short when dealing with intricate business processes that require specialized expertise, dynamic decision-making, and robust error recovery mechanisms. The financial services industry exemplifies this challenge. Market surveillance systems must coordinate multiple specialized agents to analyze trading patterns, investigate suspicious activities, and generate comprehensive reports while maintaining strict compliance and reliability standards.</p> <p>The solution combines two frameworks: <a href="https://www.langchain.com/langgraph" target="_blank" rel="noopener">LangGraph</a> for macro-level workflow orchestration and <a href="https://strandsagents.com/" target="_blank" rel="noopener">Strands</a> for intelligent agent reasoning. LangGraph excels at managing state and directed graphs for multi-agent coordination. It gives you fine-grained control over both workflow execution and state that can be shared between agents. Its central persistence layer supports features critical for production, including human-in-the-loop interactions and robust checkpoint-based recovery from failures. Meanwhile, Strands Agent serves as the reasoning engine within individual workflow nodes. It offers model-agnostic capabilities that integrate with various large language model (LLM) providers while maintaining flexible tool integration and comprehensive observability.</p> <p>With the release of <a href="https://aws.amazon.com/bedrock/agentcore/" target="_blank" rel="noopener">Amazon Bedrock AgentCore</a> last year, productionizing an agentic solution might be simplified for many use cases. The combination provides a strong foundation for production-ready agentic AI systems that can handle complex use cases while helping to deliver the infrastructure reliability and observability that enterprise applications demand.</p> <p>In this post, we demonstrate how to architect and deploy a multi-agent AI system using LangGraph and Strands on AWS infrastructure. You learn how to implement state-driven workflow orchestration with LangGraph’s checkpoint system, integrate Strands agents for specialized reasoning tasks, and use AgentCore for scalable production deployment. The complete solution is available on <a href="https://github.com/aws-samples/sample-langgraph-strands-market-analysis" target="_blank" rel="noopener">GitHub</a>.</p> <h2 id="strands-intelligent-agent-reasoning">Strands: Intelligent agent reasoning</h2> <p>Strands Agent operates on a model-agnostic architecture that adapts to your existing infrastructure without imposing architectural constraints. The agent implements an agentic reasoning loop that continuously evaluates tool outputs and makes decisions based on intermediate results, so you can build sophisticated multi-step analysis workflows. The framework includes comprehensive session and state management and multiple conversation managers to keep your context window from overflowing.</p> <p>With Strands, you can configure external tool interactions by defining tool schemas and access patterns. For our surveillance agent, we separate the discovery of data from the retrieval to avoid hallucinations and strengthen the solution against injection attacks. We use tools like <code>get_report_list</code> and <code>get_report_schema</code> to find reports and <code>run_report</code> to build the SQL query with validated parameters and run it.</p> <p>We create a <code>security_monitor</code> agent with the following tools and a system prompt:</p> <div class="hide-language"> <pre><code class="language-python">from strands import Agent, tool from strands.models.bedrock import BedrockModel model = BedrockModel( model_id="us.anthropic.claude-sonnet-4-6", re