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Agentic AI Architect - Production Operations and Enterprise Scale

Learn to deploy architecturally sound, safe agents at enterprise scale. This course covers observability, scaling, state management, CI/CD, cloud integration, monitoring, and incident response to turn demos into reliable, production-ready systems.

In partnership withPearson
Jun, 2026
6h
Advanced
Business
1.6K Students

Overview

You've designed architecturally sound agents with proper safety mechanisms. Now you need to deploy them at enterprise scale with observability, reliability, and integration into your organization's cloud infrastructure. 

Most agentic AI projects fail not because of poor agent design, but because of: 

  • Lack of proper observability and evaluation frameworks 

  • Inability to scale beyond single-user demos 

  • Poor state management and session handling 

  • No CI/CD pipeline for agent deployments 

  • Missing integration with enterprise platforms (AWS, GCP, Azure) 

  • Inadequate monitoring and incident response 

This course teaches you production operations for agentic systems - the skills that separate proof-of-concepts from systems running reliably at enterprise scale.

Skills you'll gain

Production AI agent deployment Agent scalability and performance MLOps for agentic systems AI agent observability Distributed tracing for agents

What you'll learn

  • Design and implement scalable agent architectures using horizontal/vertical scaling, load balancing, caching strategies (semantic, response, KV), auto-scaling, and cost optimization techniques to handle enterprise workloads reliably. 

  • Architect state management and persistence systems for agents including session lifecycle management, distributed state consistency, multi-agent synchronization, backup/recovery, and data architecture for production agentic systems. 

  • Implement comprehensive observability using monitoring dashboards, distributed tracing, structured evaluation frameworks (Ragas, LangSmith), A/B testing, continuous evaluation, and feedback loops for agent improvement. 

  • Deploy and integrate agents with hyperscaler platforms (AWS Bedrock/Lambda/ECS, GCP Vertex AI/Cloud Run, Azure OpenAI/Functions) following enterprise cloud architecture patterns and governance requirements. 

  • Build CI/CD pipelines, implement deployment strategies (canary, blue-green), establish incident response procedures, manage SLOs, and apply reliability engineering patterns for production agent operations. 

  • Design and implement advanced agent applications including multimodal agents (vision-language), code generation agents (with sandboxing), and domain-specific patterns (conversational, automation, decision support) with appropriate production safeguards. 

Who Should Attend

Prerequisites

"This course is designed for MLOps and DevOps engineers, cloud architects, platform engineers, SREs, production engineers, AI engineers, ML practitioners, and technical leaders who are responsible for deploying, integrating, and maintaining reliable, enterprise-scale agentic AI systems, ensuring performance, observability, and ROI.

"Learners should have completed Course 1: Agentic AI Architect – Foundations and Design, or possess equivalent knowledge of agent architectures and design. They should understand cloud platforms (AWS, GCP, or Azure) and basic services, be familiar with CI/CD and DevOps practices, have a basic grasp of monitoring, logging, and observability concepts, and experience with APIs and webhooks. Understanding system scalability and performance concepts is helpful but not required.

Chapters

Explore a structured set of chapters designed to build your skills step by step, with practical examples and hands-on applications.

You need to enroll in this course to access the curriculum. Click 'Enroll' to get started!

Segment 01: Course Introduction

Segment 02: Module Introduction Scaling Beyond Demos

Segment 03: Horizontal and Vertical Scaling Patterns for Agents

Segment 04: Load Balancing Strategies and Geographic Distribution

Segment 05: Caching Strategies Semantic Response and KV Cache

Segment 06: Latency Optimization and Performance Tuning

Segment 07: Cost Management and Monitoring

Segment 08: Auto-Scaling and Capacity Planning

Meet your instructors

Scott Cosentino

Scott Cosentino

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Frequently Asked Questions

How much do the courses at Starweaver cost?

We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.

Do you offer any certifications upon completion of a course at Starweaver?

Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

Does Starweaver offer any free courses or trials?

No, we don't offer any free courses, but we do offer 5-day trial only on our subscriptions-based plans.

Are Starweaver's courses designed for beginners or advanced students?

Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

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Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.