January 2026 Summaries

Network automation practitioners face challenges akin to building a car from scratch, with a lack of standardized blueprints impeding widespread automation adoption. The NAF (Network Automation Framework) has emerged as a solution to this problem, offering a structured approach that emphasizes functional building blocks rather than specific tools. These six components—intent, observability, orchestrator, collector, executor, and presentation—provide a universal framework applicable to any scale or level of maturity, enabling clearer understanding and communication within the industry. This framework aims to elevate practitioners' perspectives from tool-centric to architect-level, fostering a common language and shared understanding. The collaborative efforts leading to the NAF Framework have been driven by community feedback and discussions, highlighting the importance of an iterative and inclusive process. As the industry continues to evolve, the framework is poised to guide both novices and seasoned professionals, helping them avoid common pitfalls and ensuring alignment with industry standards.

Infrahub Generators provide an automated solution to the repetitive and error-prone task of manually provisioning infrastructure by allowing users to codify infrastructure patterns in Python, which can then be executed consistently and automatically. These Generators convert high-level service requests into technical implementations by applying business logic to input data, ensuring idempotency by updating existing objects as needed without duplication. They integrate with Infrahub's version control system, providing audit trails and CI pipeline validation for changes, and can be triggered manually, by events, or automatically when targeted objects are modified. Common use cases include service provisioning, new site deployment, resource allocation, maintaining derived objects, and generating data center fabric, all while leveraging resource pools for efficient allocation.

The recent discourse suggesting that AI coding tools might render SaaS and open-source obsolete overlooks the evolving dynamics in software development, where ease of building software benefits both novices and seasoned developers alike. While AI can expedite coding and prototyping, the essence of software development lies in planning, testing, documentation, and long-term maintenance, areas that still demand significant human expertise. Open-source software has shown that despite the availability of free code, the associated costs of maintenance and support lead many companies to opt for paid SaaS solutions. The advent of AI shifts advantages to those who can effectively blend AI tools with domain expertise and sound architecture, enhancing the capabilities of professional teams and open-source communities. OpsMill exemplifies this shift by using AI to advance infrastructure automation, aiming to provide reliable building blocks that integrate documentation, configurations, and automation, while acknowledging that sustainable and reliable systems still rely on human expertise. AI is not eliminating SaaS or open-source but is enabling the creation of more robust and reliable software products.

Infrahub Transformations and artifacts offer a comprehensive solution for automating data transformation and output generation from infrastructure data, essential for creating device configurations, exporting data for third-party tools, and generating reports. Transformations utilize GraphQL queries to extract infrastructure data, which is then processed and formatted using either Jinja2 templates for text-based outputs or Python code for structured data like JSON payloads. These Transformations are stored in Git repositories, enabling version control, testing, and collaboration, and they seamlessly integrate with Git workflows, allowing for development and production consistency. Artifacts, the outputs of Transformations, are cached and stored in Infrahub’s object storage, providing a complete history and enabling changes to be reviewed collaboratively before deployment. They support various formats, including JSON, YAML, and CSV, and automatically regenerate when source data changes, ensuring outputs reflect the current infrastructure state. Infrahub provides tools for testing and validating Transformations and artifacts, and offers various resources for users to get hands-on experience, including documentation, webinars, and a sandbox environment.

Infrahub Resource Manager is an automation tool designed to simplify and streamline the allocation of network resources, such as IP addresses, VLAN IDs, and BGP ASNs, from predefined pools, eliminating manual tracking and preventing resource conflicts. The tool supports infrastructure-as-code workflows by automating the allocation process, ensuring complete visibility into what resources are allocated and available, and preventing duplicate assignments across different branches. It offers branch-agnostic resource allocations, which means a resource allocated in any branch is reserved across all branches, thus avoiding merge conflicts. The Resource Manager is versatile, managing three primary types of resource pools—IP address pools, IP prefix pools, and number pools—and it integrates seamlessly with Infrahub’s schema system, allowing for idempotent allocation that is critical for automation. By providing weighted allocation, the Resource Manager lets users control resource allocation order, supporting gradual migrations and capacity planning. The tool can be accessed through Infrahub's Python SDK and GraphQL API, making it adaptable for various automation scenarios in infrastructure deployment workflows.

Event-driven automation (EDA) leverages systems like Infrahub and Ansible to automatically respond to changes without manual intervention, as demonstrated by a practical example of managing router interfaces. Infrahub is used to model infrastructure data and track changes, emitting events through webhooks when specific changes occur. Ansible, equipped with its rulebooks and playbooks, listens for these events and triggers actions based on predefined rules. This approach allows for dynamic inventory management and the execution of configuration changes, such as toggling the status of a router interface, using Infrahub as the source of truth. The example illustrates how Infrahub and Ansible can be integrated to create a responsive, automated infrastructure management system. By extending this pattern, similar event-driven automation can be applied to various tasks and device types to achieve scalable and efficient operations.

Infrahub 1.7 introduces enhancements designed to improve its functionality as a reliable system of record for infrastructure data, focusing on auditability, reusability through Profiles, and an improved user experience. New features include audit metadata for every object to enhance data governance and accountability, accessible through the UI, GraphQL API, and Python SDK. Profiles now support relationships, enabling the consistent application of configurations across multiple objects, while also allowing for individual customization. The object detail view has been redesigned for more efficient access and action, and the branch list page has been updated to handle large-scale environments more effectively. The update also modernizes core backend dependencies, ensuring better maintainability, security, and a solid foundation for future development.