In this post, we provide a solution to build highly scalable, serverless multi-agent generative AI systems on AWS using LangGraph Agents as orchestrators integrated with Amazon Bedrock AgentCore Memory and Amazon Bedrock AgentCore Observability.

Our approach for building highly scalable serverless multi-agent orchestrations combines serverless technologies such as AWS Lambda and AWS Step Functions. These services can be used by developers to build LangGraph agents that scale automatically, respond to events in real time, and remove infrastructure management. This makes them ideal for dynamic, bursty agent workloads. By combining these services, you can orchestrate complex multi-tool agent workflows with durable state management, retries, and fine-grained cost control.

LangGraph’s explicit graph-based execution model enables deterministic coordination, parallelism, and conditional routing between agents, making complex multi-agent workflows more straightforward to reason and debug. By separating orchestration logic from agent behavior, you can use LangGraph to add, remove, or evolve specialized agents independently while maintaining a clear, auditable execution path. This is especially valuable for production systems that require predictable behavior, extensibility, and structured control over multi-agent reasoning.

AgentCore Observability extends these capabilities by providing detailed visibility into each invocation, capturing model inputs/outputs, latency, and tool-chain metrics across distributed serverless components. Integrated memory services from AgentCore Memory enable agents to maintain short-term conversational context and long-term knowledge across sessions.

Our serverless LangGraph and AgentCore based multi-agent orchestration system solution is a generative AI-powered multi-agent campaign review system that orchestrates human reviews using diverse personas that enable marketing campaigns to resonate authentically with target audiences while maintaining legal alignment and brand standards. It consists of three specialized AI agents that analyze the marketing campaign in parallel – a persona reviewer agent reviews content from diverse demographic perspectives and provides resonance scoring, a validator agent verifies legal alignment and brand guideline adherence, while a finalizer agent then synthesizes feedback into actionable recommendations. Users upload campaign documents through a React frontend that also polls for results and displays reviews as they become available.