AI Agents Operations and Control Planes
Master management, monitoring, and security of intelligent autonomous agents. Learn to design, deploy, and operate AI agents safely and efficiently in production environments.
What You'll Learn
By the end of this track, you'll have the skills to architect, implement, and operate sophisticated AI agent systems in production.
AI Agent Architecture
Understand the architecture of AI agents and master central control concepts including governance, policy, and enforcement mechanisms.
Control Planes Design
Learn to plan and implement complex control planes including management, control, data, and security planes for agent systems.
Production Monitoring
Master monitoring, logging, and observability of agents in production environments using industry-standard tools and practices.
Security & Guardrails
Implement safety mechanisms, guardrails, and access controls to ensure agents operate securely within defined boundaries.
Curriculum
1
Fundamentals of AI Agents and Control
180 minutes
Fundamentals of AI Agents and Control
180 minutes
Introduction to autonomous agents and use cases
AI agent architecture — components and relationships
Control concepts: Governance, Policy, and Enforcement
Comparison of different control types
2
Designing Control Planes
240 minutes
Designing Control Planes
240 minutes
Management Plane — agent lifecycle management
Control Plane — defining and enforcing policies
Data Plane — action execution and communication
Security Plane — trust and authentication
3
Operating and Managing Agents in Production
210 minutes
Operating and Managing Agents in Production
210 minutes
Deployment and scaling of agents
Health checks, heartbeats, and self-healing
Resource management and cost optimization
Update strategies: Blue-Green, Canary, Rolling
4
Monitoring, Logging, and Observability
200 minutes
Monitoring, Logging, and Observability
200 minutes
Setting up observability stacks for agents
Critical metrics: latency, throughput, error rates
Distributed tracing across agents
Alerting and automated incident response
5
Security and Authorization in Control
200 minutes
Security and Authorization in Control
200 minutes
Authentication and authorization for agents
Role-Based Access Control (RBAC) for actions
Audit logging and anomaly detection
Sandboxing and agent isolation
6
Constraints, Guardrails, and Fallback Controls
210 minutes
Constraints, Guardrails, and Fallback Controls
210 minutes
Defining boundaries and mission scope for agents
Implementing rate limiting and resource quotas
Human-in-the-loop and approval workflows
Automatic rollback and emergency stops
7
Orchestration and Multi-Agent Coordination
180 minutes
Orchestration and Multi-Agent Coordination
180 minutes
Multi-agent coordination patterns
Consensus mechanisms and distributed decision-making
Event-driven architecture for agents
Workflow engines and DAG-based execution
8
Case Studies and Complex Problem Solving
240 minutes
Case Studies and Complex Problem Solving
240 minutes
Case study 1: E-commerce agent control system
Case study 2: Autonomous vehicle decision control
Troubleshooting: deadlocks and cascading failures
Best practices and industry lessons learned
Frequently Asked Questions
What prior knowledge do I need?
Basic knowledge of distributed systems, familiarity with microservices architecture, and experience with APIs will be helpful. Background in Python or Go will add value to hands-on exercises.
Does this track include hands-on projects?
Yes, this track includes 4 hands-on projects: building a simple control plane, setting up observability infrastructure, implementing security policies, and designing a multi-agent architecture. Each project reinforces the concepts learned in the modules.
How is this different from a regular AI course?
This track focuses on operations and management of agents, not model fine-tuning or optimization. We cover how to run agents safely, reliably, and efficiently in production environments — the operational side of AI systems.
Will this course cover specific technologies?
Yes, we cover Kubernetes, Prometheus, Jaeger, OpenTelemetry, and policy management tools. We also use OpenAI, Anthropic, and local LLM examples to demonstrate real-world agent implementations.
Is this track theoretical or practical?
It's 50/50 theory and practice. Each module includes lectures, architecture diagrams, live demos, and hands-on practice sessions with real code and tools.
How much time should I spend on self-study?
Expect 4–6 hours per week for self-study, hands-on code exploration, and exercises. Capstone projects require 15–20 hours each depending on your depth of implementation.
What will I receive upon completion?
You'll receive a course completion certificate, a GitHub repository with 4 production-ready projects, and an architecture document for designing control systems for agents.
Is this course current for 2025?
Yes. The course covers latest models (GPT-4o, Claude 3.5, Gemini 2.0), modern agent frameworks, and current industry trends in agentic AI for 2025.
Ready to master AI agent operations?
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