Building a Multi-Agent Manufacturing System: A Step-by-Step Tutorial with AWS Bedrock, Qwen 3.7 Max, and Llama 5

Introduction: Multi-Agent Systems in Manufacturing Factory floors are under constant pressure to boost throughput, reduce downtime, and maintain quality—all while supply chains grow more volatile. Traditional rule-based scheduling and manual inspection can't keep pace. Multi-agent AI systems, where specialized agents collaborate autonomously, offer a new path. As of May 24, 2026, the convergence of powerful large language models (LLMs) and cloud-native orchestration tools makes it feasible for operations leaders to pilot such systems without a massive R&D investment. This multi-agent manufacturing system tutorial provides a vendor-neutral, step-by-step guide for B2B operations leaders. You'll learn how to build a two-agent system on AWS Bedrock: a Job Scheduling Agent powered by Qwen 3.7 Max and a Quality Control Agent using Llama 5. We'll cover architecture, inter-agent communication vi

a Amazon Bedrock Agent Collaboration, and deployment on Kubernetes. The tutorial is grounded in a synthetic pilot—inspired by real industry feedback—so you can assess feasibility for your own factory floor. Architecture Overview: Agents, Models, and AWS Bedrock At a high level, the system consists of two intelligent agents, a shared communication fabric, and a containerized runtime (Figure 1). Figure 1: High-level architecture of the multi-agent manufacturing system. Both agents run as microservices on a Kubernetes cluster (e.g., Amazon EKS). They invoke LLMs hosted on AWS Bedrock , which provides fully managed access to foundation models. The key enabler is Amazon Bedrock Agent Collaboration (generally available since early 2026), which lets multiple agents share context, delegate tasks, and maintain a unified session state—essential for coordinating scheduling and quality decisions. Qw

en 3.7 Max (Alibaba Cloud, released March 2026) excels at complex planning, reasoning over constraints, and generating optimized schedules. On Bedrock, it's available via the model ID. Llama 5 (Meta, open-source, available on Bedrock since April 2026) brings state-of-the-art multimodal capabilities, making it ideal for visual defect detection and quality classification. This architecture is event-driven: the Scheduler Agent reacts to new production orders and machine status changes, while the Quality Agent processes inspection images on demand. Collaboration ensures that quality issues immediately feed back into rescheduling decisions. Agent Roles: Job Scheduling with Qwen 3.7 Max and Quality Control with Llama 5 Job Scheduling Agent The Scheduler Agent receives a stream of production orders—each with a due date, required operations, and bill of materials—along with real-time machine ava

ilability. Using Qwen 3.7 Max, it: Parses natural-language order specifications (e.g., "500 units of part X by Friday"). Generates a feasible job sequence that minimizes makespan and tardiness. Handles disruptions (machine breakdowns, rush orders) by re-optimizing on the fly. Communicates with the Quality Agent to factor in rework loops when defects are detected. We chose Qwen 3.7 Max because of its strong performance on planning benchmarks and its ability to follow complex, multi-step instructions. In our synthetic pilot, it consistently produced schedules within 2 seconds, even for 100-job batches. Quality Control Agent The Quality Agent ingests high-resolution images from production line cameras. Llama 5 processes each image and returns a structured defect report: type, severity, and bounding box. The agent then: Classifies the product as pass, rework, or scrap. Logs the result in the

factory data lake. If a defect is found, it triggers a collaboration request to the Scheduler Agent, which may insert a rework operation or adjust downstream jobs. Llama 5 was selected for its vision-language capabilities and open-weight availability, which allows fine-tuning on proprietary defect datasets if needed. On Bedrock, it's accessed via the model ID, offering a balance of accuracy and latency. Inter-Agent Communication: Using Amazon Bedrock Agent Collaboration Traditional multi-agent systems often rely on custom message queues or REST APIs. Amazon Bedrock Agent Collaboration simplifies this by providing a managed, stateful communication layer. Each agent is defined with an agent instruction and action groups . When one agent needs another, it issues a collaboration request that includes a natural-language task and optional structured data. Setting Up Agent Collaboration Here's

a simplified Python (boto3) snippet to create the two agents and enable collaboration: In production, you would also attach action groups to connect agents to your MES, databases, and camera APIs. The collaboration fabric handles session state, so a quality inspection result is automatically linked