Agentic AI in Action: Shaping the Future of Procurement and Supply Chain

The world of supply chain and procurement is changing fast. Companies face rising costs, uncertain demand, and complex global networks, and traditional methods of managing these challenges are becoming less effective. 

That’s where Agentic AI comes in. 

Unlike standard AI, which only provides recommendations or insights, Agentic AI can take independent actions to solve problems and make decisions. This article explores how Agentic AI is reshaping procurement and supply chain management in the U.S. and why companies should pay attention.

What is Agentic AI?

Agentic AI is a type of artificial intelligence designed to act autonomously. It can observe a situation, understand the options available, and take actions without needing step-by-step human instructions. The AI can identify issues, evaluate solutions, and execute decisions to improve operations in a supply chain or procurement setting.

For example, Agentic AI can automatically source suppliers, compare prices, and place orders in procurement. In supply chains, it can reroute shipments or adjust inventory levels based on real-time demand. This ability to act independently allows organizations to respond faster to changes and reduce the burden on human employees.

Why Procurement and Supply Chain Need Agentic AI

U.S. businesses operate in a highly competitive and interconnected market. Supply chains are global, and disruptions can occur anywhere, from a factory overseas to a local supplier facing labor shortages. Procurement teams must balance cost, quality, and delivery speed, while supply chain managers need to ensure goods move efficiently from suppliers to customers.

Manual processes and human-led decision-making are often too slow to respond to rapid changes. Mistakes or delays can cost millions in lost sales, missed deadlines, or unnecessary inventory. Agentic AI offers a way to manage these challenges more effectively by acting quickly and using data-driven insights.

Applications of Agentic AI in Procurement

1. Supplier Selection and Management: Finding the right suppliers is one of the most challenging tasks in procurement. Agentic AI can evaluate thousands of suppliers, comparing price, reliability, delivery times, and risk profiles. It can then automatically shortlist the best options and even initiate contact.
2. Negotiation Support: Negotiating contracts and pricing can be time-consuming. Agentic AI can analyze past deals, current market conditions, and supplier behavior to suggest optimal terms or directly negotiate simple agreements within pre-set limits.
3. Purchase Automation: For routine purchases, Agentic AI can automatically create orders, track delivery, and handle reorders. This reduces administrative work and ensures stock levels are maintained efficiently.

Applications of Agentic AI in Supply Chain

The client manufactures precision brake assemblies for electric vehicles. Their production lines handle 20,000+ units daily, supplying to Tier-1 OEMs in the U.S. and Canada.

Challenge Statement

Despite deploying 25 human inspectors and some vision-based systems, their defect detection accuracy was just 80–83%. Missed defects were leading to:

• Warranty claims and penalties from OEM partners.
• Over $2.3 million in annual scrap and rework costs.
• Production delays occurred whenever inspections became bottlenecks.

They needed a scalable AI-driven QC solution seamlessly integrated into their existing manufacturing execution system (MES).

Mactores' Solution

Mactores designed an agentic AI-powered pipeline using Amazon SageMaker.

• Data Acquisition
  

• • Installed industrial-grade cameras at three inspection points on each line.
  • Collected nearly 1.2 million images (both defective and acceptable parts) over six weeks.

• Data Preparation & Labeling
  

• • Leveraged Amazon SageMaker Ground Truth to label images with defect categories: surface scratches, cracks, misalignments, and structural deformities.
  • Enriched datasets with metadata such as machine ID and shift timing.

• Model Development & Training
  

• • Fine-tuned a ResNet-based image classification model within SageMaker.
  • Used spot instances for cost optimization.
  • Implemented active learning loops, allowing the model to request labeling on ambiguous images.

• Agentic AI Integration
  

• • Introduced an agent controller capable of:
    
    • Deciding when retraining should occur.
    • Escalating uncertain cases to human inspectors.
    • Adjusting detection thresholds based on defect rates.

• Deployment & Monitoring
  

• • Deployed the model via SageMaker Endpoints.
  • Integrated directly into the MES to trigger accept/reject decisions in real time.
  • Used Amazon CloudWatch to monitor inference latency and data drift.
    
    

Outcomes

Six months post-deployment, the client reported:

• Accuracy improvement: defect detection rose to 96.8%.
• Cost savings: scrap and rework costs dropped by $1.9M annually.
• Throughput increase: inspections became 15% faster, eliminating bottlenecks.
• OEM satisfaction: warranty claims decreased significantly, strengthening supplier relationships.
  
  

What stood out most to the client was that the system was not static—it kept learning and improving, reducing dependency on retraining cycles.

The Bigger Picture

Automating quality control with Amazon SageMaker can help you build a production line that thinks for itself. Manufacturers often navigate tight margins, strict compliance, and rising customer expectations. This shift delivers a measurable impact on both profitability and competitiveness.

At Mactores, we help manufacturers design agentic AI-driven systems that don't just detect defects but evolve with production needs. If your team is stuck in endless quality control cycles and struggling to manage recurring defects, it's time to explore a more innovative approach.

Let's talk. Share your challenges with us, and we'll help you build an intelligent agent that makes quality checks faster, more reliable, and far less of a bottleneck.

FAQs

• How can Amazon SageMaker improve quality control in manufacturing?
  Amazon SageMaker improves manufacturing quality control by using machine learning models to detect real-time defects, automate inspections, and reduce reliance on manual checks. This ensures consistent accuracy, faster throughput, and lower defect-related costs.
• What is agentic AI in quality control?
  Agentic AI adds autonomy to quality control systems by allowing models to retrain, adapt to new defect patterns, and escalate uncertain cases without human intervention. This makes manufacturing inspection more innovative, scalable, and continuously evolving.
• Can agentic AI be applied to other manufacturing processes beyond quality control?
  Yes. While Amazon SageMaker is powerful for automating quality inspections, agentic AI can streamline scheduling, predictive maintenance, and supply chain coordination. These intelligent systems don't just follow rules—they adapt to changing conditions across the enterprise. Explore our blog on The Agentic Future of Automating Tasks for Enterprises to see how agentic AI reshapes automation at scale.
• What's the foundation needed to scale AI-driven quality control systems?
  Automating quality control is only as strong as the data powering it. Manufacturers need reliable pipelines, clean datasets, and a robust integration layer to support agentic AI. Even the best models will struggle to deliver consistently without strong data foundations.