“This course contains the use of artificial intelligence”

AI systems are no longer limited to generating text or answering questions. They are becoming autonomous actors — capable of reasoning, using tools, making decisions, and operating continuously inside real business systems. This shift introduces a new challenge: how do you design, operate, govern, and scale AI agents safely in production?

That is exactly what AgenticOps Foundations is designed to teach.

This course goes far beyond prompts, chatbots, and demos. You will learn how real-world agentic systems are architected, deployed, monitored, and governed inside enterprise environments. We treat AI agents not as experiments, but as operational systems that must meet requirements for reliability, cost control, security, privacy, compliance, and auditability.

You will start by building the correct mental model for AgenticOps, understanding how it extends beyond DevOps and MLOps into a new operational discipline built for autonomous decision-making systems. From there, the course dives deeply into agent architecture, covering perception, memory, reasoning, tool execution, and lifecycle management.

As the course progresses, you will design multi-agent systems, learn orchestration patterns, implement memory and retrieval architectures, and understand how agents interact with real data pipelines, events, and infrastructure. You will also learn how to make agent behavior observable, debuggable, and measurable using telemetry, metrics, and structured logging.

A major focus of this course is safety and governance. You will learn how to prevent hallucinated actions, control tool misuse, enforce policy-aware constraints, protect PII, design secure memory systems, and build agents that are audit-ready by design. These are the capabilities enterprises require before granting agents real autonomy.

Finally, the course shows you how to move from prototype to production at scale. You will learn cost optimization strategies, infrastructure patterns, organizational ownership models, and change management approaches that allow agentic systems to be adopted safely across teams.

By the end of this course, you won’t just know how agents work — you’ll know how to run them responsibly in the real world.

This course is ideal for engineers, architects, and technical leaders who want to stay ahead of the shift toward AI-native operational systems and build the skills required for the next generation of enterprise AI.

Who this course is for:

• Software engineers and backend developers who want to build AI agents that go beyond chatbots and operate reliably in production systems.
• AI, ML, and data professionals looking to understand how agentic systems are architected, governed, and deployed at enterprise scale.
• Platform, DevOps, and MLOps engineers who need to support autonomous agents with proper infrastructure, observability, cost control, and safety mechanisms.
• Technical architects and system designers responsible for making long-term decisions about AI-native platforms and operational models.
• Product managers and technical leaders who work closely with AI teams and need a practical understanding of agent capabilities, limitations, and risks.
• Career-focused learners and early adopters who want to future-proof their skills by learning AgenticOps—an emerging discipline shaping the next generation of AI systems.