AgenticOps: Designing AI-Native Autonomous Systems

Name: AgenticOps: Designing AI-Native Autonomous Systems
Availability: InStock
Rating: 4.00 (264 reviews)

4.00

2,645 students

4h 36m

Updated Feb 2026

What you'll learn

Design and architect autonomous AI agents with clear separation of reasoning, memory, tool execution, and lifecycle management for real-world systems.

Build and orchestrate multi-agent systems using proven patterns such as planner-executor, manager-worker, and critic-reviewer architectures.

Implement safe and governed agent behavior by applying guardrails, policy constraints, human-in-the-loop checkpoints, and least-privilege access models.

Create production-ready agent infrastructure using containers, serverless execution, queues, and event-driven orchestration.

Design memory and retrieval systems for agents, including short-term vs long-term memory, vector databases, and enterprise-grounded RAG pipelines.

Monitor, debug, and evaluate agent behavior using telemetry, metrics, replay, simulation, and continuous evaluation pipelines.

Optimize agent systems for cost and scale, applying token budgets, throttling, caching strategies, and performance trade-offs.

Prepare agentic systems for enterprise adoption, covering governance, compliance, privacy, organizational ownership, and operational integration.

Course Description

“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.

Requirements

Basic familiarity with programming concepts is helpful but not mandatory. You should understand ideas like functions, APIs, and data flow at a conceptual level.
General awareness of AI or machine learning (such as having used ChatGPT or worked with LLMs at a basic level) is recommended, but you do not need prior experience building AI systems.
No prior experience with AgenticOps, multi-agent systems, or orchestration frameworks is required. All core concepts are explained from the ground up.
No advanced mathematics or deep ML theory is needed. This course focuses on system design, architecture, operations, and real-world deployment patterns.
A computer with internet access is sufficient. Any modern operating system (Windows, macOS, or Linux) will work.
Optional but helpful: exposure to cloud platforms, DevOps, or backend systems — though these are introduced in a practical, beginner-friendly way.