June 2026 Summaries

OpsMill is now the steward of the Nornir project, a Python-native infrastructure orchestration framework that has become essential for many infrastructure teams over the past eight years. Emphasizing its commitment to keeping Nornir open-source and community-driven, OpsMill plans to modernize and support the project while fostering collaboration with the community to shape its future. Founded by David Barroso, who created Nornir and previously developed NAPALM, the project benefits from his vision of a framework that simplifies network automation without introducing new complexities. OpsMill values Barroso's ongoing role as a Technical Advisor, and many of its team members have longstanding involvement with Nornir, contributing to its success and supporting the open-source community. The company invites feedback from users and contributors to better understand their needs and explore potential commercial support offerings, demonstrating its dedication to ensuring Nornir's continued growth and relevance in the evolving landscape of infrastructure automation.

Infrahub 1.10 brings significant enhancements to network management by introducing Graph Traversal, which allows users to explore network dependencies without custom queries, and improving identity and access management with native LDAP support for streamlined user onboarding. The update also optimizes automation by ensuring only necessary artifacts are regenerated after changes, thus maintaining fast and reliable repositories. Additionally, Infrahub Sync 2.0 increases synchronization efficiency by processing only changed records, while the integration of agents and AI Skills into workflows aids in schema creation, dependency analysis, and data auditing. The MCP Server facilitates the use of Infrahub data across environments, and developer experience is enhanced through machine-readable GraphQL errors and improved schema design controls. The release requires attention to breaking changes documented in its release notes.

AIOps, or Artificial Intelligence for IT Operations, is an appealing concept where AI agents autonomously manage and optimize network operations, yet its practical implementation faces significant challenges. Current AI agents often malfunction unless tasked with highly specific duties, causing skepticism and reluctance to deploy them in production environments. This mirrors past experiences with network automation, where rushed implementations led to fragmented solutions due to poor foundational planning. A successful approach involves modeling intent, structuring data, and prioritizing automation only after establishing a robust foundation. The Network Automation Framework (NAF) offers a blueprint for scalable, reliable network automation, emphasizing six foundational elements: intent, executor, observability, collector, orchestrator, and presentation. These elements support a structured, tool-agnostic, and continuously updated framework that is accessible to various stakeholders, not just network engineers. The NAF Framework provides a systematic approach to network automation, enabling seamless integration of AI while ensuring reliability and scalability through thorough planning and execution.

The narrative highlights the challenges faced by organizations trying to integrate AI agents into their operations, particularly when dealing with outdated and fragmented data models. It draws attention to the common issues such as the lengthy process of updating schemas, reliance on complex and often makeshift solutions, and the lack of a unified source of truth which results in inefficiencies and errors. The text emphasizes the necessity for a comprehensive, flexible, and well-maintained data model that provides both technical and business context to enable AI agents to function effectively. Examples from industries like telecommunications and global companies such as TikTok and Eurofiber illustrate the problems of misaligned data models, the need for human intervention, and the importance of having a data model that accurately reflects the current state and purpose of the infrastructure to avoid costly mistakes and to fully leverage automation capabilities.

OpsMill has developed a decentralized documentation system where each team is responsible for maintaining the documentation alongside the code in their respective repositories, addressing the common issue of outdated documentation due to unclear ownership. This approach utilizes a push-based sync mechanism, identified as Option C, where changes in documentation automatically update a central Docusaurus site, ensuring that the documentation remains current and aligned with the code changes. The system avoids the pitfalls of a monolithic documentation repository and cumbersome git submodule management by allowing each project's documentation to be self-contained while contributing to a unified platform. The setup includes a multi-repo architecture with individual plugin instances for each project within the Docusaurus site, enabling seamless integration and search functionality across projects. This method enhances the consistency and accuracy of the documentation, benefiting customers with up-to-date information and streamlining processes for engineers and new joiners. OpsMill emphasizes the importance of detailed configuration and scripting to maintain consistency across multiple repositories, with the goal of facilitating efficient onboarding and information dissemination.

Infrahub is positioned as an automation-focused infrastructure platform that diverges from the traditional asset inventory approach by prioritizing a context-heavy source of truth for network management. Unlike asset management systems that track ownership, cost, and lifecycle of hardware for financial and procurement purposes, Infrahub emphasizes understanding the intended and actual state of a network to facilitate reliable automation and AI applications. This approach allows for effective configuration generation, service provisioning, and drift reconciliation by integrating device and warranty data into its system, ensuring that finance, procurement, and network automation can collaborate effectively. Infrahub achieves this integration through Infrahub-sync, which imports essential data from IT Asset Management (ITAM) or Configuration Management Database (CMDB) systems to enable informed decision-making for network changes while maintaining a clear distinction between asset tracking and network modeling.

OpsMill and its platform, Infrahub, are pioneering a shift in the infrastructure management landscape by emphasizing a version-controlled, policy-aware system of record that integrates governance and flexibility. As the industry gravitates toward these features, OpsMill's approach validates the necessity of a robust infrastructure source of truth, especially in the context of AI-driven network automation. Infrahub's key features include a flexible, user-defined schema that adapts to dynamic infrastructure needs, native version control for safe and auditable changes, and built-in governance to streamline validation and deployment processes. This infrastructure model is designed to accommodate evolving technologies and ensure seamless integration between technical data and business logic, addressing challenges posed by AI data centers' complexity and rapid advancements. As companies evaluate their data management platforms, they are encouraged to consider solutions like Infrahub that are purpose-built for this transition, promising to redefine capabilities over the coming years.