Designing, Deploying and Managing Network Automation Systems

Overview

The Designing, Deploying and Managing Network Automation Systems (AUTOCOR) course equips network professionals with the skills required to design, build, deploy, and manage modern network automation solutions using Cisco technologies and industry-standard automation tools. The course combines software development principles with infrastructure automation, enabling learners to automate configuration management, streamline operations, and improve the reliability of enterprise networks.

Throughout the course, learners develop practical skills in Python, REST APIs, Ansible, Terraform, Infrastructure as Code (IaC), Git, GitLab CI/CD pipelines, Cisco Modeling Labs (CML), pyATS, model-driven telemetry, Docker Compose, secure automation practices, and the integration of Generative AI and Large Language Models (LLMs) into network automation workflows. The course also prepares learners for the Cisco 350-901 AUTOCOR v2.0 certification exam while developing practical skills that can be applied immediately within enterprise environments.

Prerequisites

Participants should have:

• A solid understanding of enterprise networking concepts and Cisco networking technologies.
• Experience configuring and managing Cisco network devices.
• Basic Python programming knowledge.
• Familiarity with REST APIs and network programmability concepts.
• Experience using the command line in Linux or similar operating systems is recommended.
• Knowledge equivalent to Cisco CCNA and introductory network automation training is beneficial.

Target audience

This course is designed for:

• Network automation engineers.
• Network engineers moving into automation and programmability roles.
• DevOps engineers working with network infrastructure.
• Systems engineers responsible for network deployment and operations.
• Infrastructure architects implementing Infrastructure as Code practices.
• Professionals preparing for the Cisco Certified Specialist – Automation Core certification or the CCNP Automation certification pathway.

Objectives

By the end of this course, learners will be able to:

• Evaluate modern network automation frameworks, tools, and deployment approaches.
• Develop Python scripts to automate operational networking tasks.
• Build automation workflows using REST APIs, RESTCONF, and YANG data models.
• Create Ansible playbooks to automate network configuration and operational tasks.
• Develop Infrastructure as Code solutions using Terraform.
• Implement version control and collaborative workflows using Git.
• Design automated CI/CD pipelines using GitLab for network testing and deployment.
• Integrate Cisco Modeling Labs into automated validation workflows.
• Validate network configurations using pyATS.
• Configure and use model-driven telemetry for operational monitoring.
• Troubleshoot common automation failures using structured logging and debugging techniques.
• Apply secure coding practices to protect automation solutions.
• Deploy containerised automation environments using Docker Compose.
• Explain how Generative AI, AI agents, and MCP servers can support intelligent network automation workflows.
• Prepare for the Cisco 350-901 AUTOCOR v2.0 certification exam.

Outline

Network automation foundations

• Understanding network automation architectures
• Evaluating automation frameworks and toolsets
• Selecting the appropriate automation approach
• Automation design considerations and best practices

Python for network automation

• Python fundamentals for network engineers
• Automating CLI-based operational tasks
• Working with structured data
• Error handling and logging
• Creating reusable automation scripts

REST APIs and network programmability

• REST API fundamentals
• API authentication and security
• Working with JSON data
• Automating network tasks through APIs
• RESTCONF and YANG model integration

Network automation with Ansible

• Installing and configuring Ansible
• Creating inventories and playbooks
• Automating configuration management
• Managing network devices at scale
• Reusable automation workflows

Infrastructure as Code with Terraform

• Infrastructure as Code principles
• Terraform architecture
• Creating providers and resources
• Automating network infrastructure deployment
• Managing infrastructure lifecycle

Version control with Git

• Git repositories and workflows
• Branching strategies
• Managing configuration changes
• Collaborative development
• Tracking automation projects

CI/CD for network automation

• Continuous Integration concepts
• Continuous Deployment workflows
• Building GitLab CI pipelines
• Automated testing and validation
• Deployment automation best practices

Cisco Modeling Labs integration

• Creating virtual network environments
• Integrating CML into automation workflows
• Automated lab deployment
• Test environment management
• Validation before production deployment

Network validation with pyATS

• pyATS architecture
• Automated operational testing
• State validation
• Health checks
• Reporting and troubleshooting

Model-driven telemetry

• Streaming telemetry fundamentals
• Collecting operational data
• Monitoring network health
• Data visualisation considerations
• Operational analytics

Troubleshooting network automation

• Identifying automation failures
• Debugging Python automation
• Troubleshooting Ansible playbooks
• Diagnosing REST API issues
• Using structured logging for problem resolution

Secure network automation

• Secure coding principles
• Protecting credentials
• Input validation
• API security
• Certificate management
• Deploying trusted TLS certificates

Containerised automation environments

• Docker fundamentals
• Docker Compose architecture
• Multi-service automation environments
• Portable development environments
• Automation lifecycle management

Artificial intelligence for network automation

• Generative AI concepts
• AI-assisted script generation
• AI agents for operational automation
• Integrating LLMs using MCP servers
• Benefits, limitations, governance, and security considerations

Certification preparation

• Review of exam domains
• Practical implementation scenarios
• Knowledge checks
• Exam preparation guidance
• Best practices for certification success.

Exams and assessments

This course includes instructor-led practical exercises, knowledge checks, and hands-on activities throughout the programme to reinforce learning. Learners preparing for certification should sit the Cisco 350-901 AUTOCOR v2.0 examination separately. Successful completion contributes towards the Cisco Certified Specialist – Automation Core certification and the CCNP Automation certification pathway.

Hands-on learning

This course includes: Practical Python scripting exercises.

• REST API integration labs.
• Ansible and Terraform automation labs.
• Git and GitLab CI/CD pipeline development.
• Cisco Modeling Labs integration exercises.
• pyATS validation activities.
• Docker Compose implementation.
• AI-assisted automation scenarios.
• Real-world troubleshooting exercises based on enterprise networking environments.