Design, scale, and govern autonomous AI agents for real-world enterprise systems
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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.
Explore related topics
This course includes:
- 4.5 hours on-demand video
- Access on mobile and TV
- Full lifetime access
- Certificate of completion
Course content
12 sections • 34 lectures • 4h 36m total length
- 1.1 What Is AgenticOps?
- 1.2 Agents vs Models vs Pipelines
- 1.3 Real-World Use Cases
- 2.1 Anatomy of an Agent
- 2.2 Agent State & Lifecycle
- 2.3 Tool Calling & Action Execution
- 3.1 Multi-Agent Design Patterns
- 3.2 Coordination & Communication
- 3.3 Agent Orchestration Engines
- 4.1 Short-Term vs Long-Term Memory
- 4.2 Retrieval-Augmented Agents
- 4.3 Knowledge Freshness & Drift
- 5.1 Agent-Driven Data Pipelines
- 5.2 Event-Driven Agent Systems
- 5.3 Observability for Data Agents
- 6.1 Failure Modes in Agentic Systems
- 6.2 Guardrails & Constraints
- 6.3 Safety-First Agent Design
- 7.1 Agent Telemetry
- 7.2 Metrics for Agent Performance
- 7.3 Debugging Agent Failures
- 8.1 Testing Agent Behavior
- 8.2 Offline vs Online Evaluation
- 8.3 Continuous Evaluation Pipelines
- 9.1 Accountability in Autonomous Systems
- 9.2 Policy-Aware Agents
- 9.3 Privacy & Data Protection
- 10.1 Cost Control & Optimization
- 10.2 Infrastructure for Agent Systems
- 10.3 Organizational Adoption
- 11.1 Agent Framework Landscape
- 11.2 Integrating with Existing Systems
- 11.3 Avoiding Anti-Patterns
- Certificate
- 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.
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.
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.
Note: The course is free for a limited time and limited to 500 users. Enroll as soon as possible. If it is no longer free, it means you are late, and it has already exceeded its limits.
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