July 2025 Summaries

Infrahub Resource Manager is an innovative tool designed to automate the allocation and management of infrastructure resources such as IP addresses, VLAN IDs, and BGP ASNs, eliminating the inefficiencies and errors associated with manual resource management. By automatically distributing resources from managed pools and ensuring each allocation is unique and tracked, it supports network design and automation tasks like data center expansion and device provisioning. Through a user-friendly interface, Python SDK, or GraphQL, users can create resource pools, allocate resources, and manage prefixes with features like idempotent behavior and weighted allocation for efficient resource distribution. For instance, weighted allocation prioritizes resource usage from higher-weight prefixes, optimizing network resource management. The Resource Manager also supports numeric pools for managing VLAN IDs and BGP ASNs, assigning these automatically to prevent conflicts. By using Infrahub, organizations can streamline their workflows, reduce complexity, and confidently manage shared network resources, all while preparing for future growth through careful pool planning and monitoring.

Infrahub has transitioned its open-source license from the AGPL to the more permissive Apache 2.0 to address concerns raised by enterprise users and to increase accessibility and adoption. The AGPL license required users to share any code modifications with the community, which posed legal and operational challenges for some companies. A LinkedIn poll indicated that nearly half of the respondents had concerns about AGPL, and 30% often avoided it. By adopting Apache 2.0, Infrahub aims to remove these barriers, allowing users to leverage its features without needing to modify or share the core code, and eliminating the need for contributors to sign a Contributor License Agreement. This change reflects Infrahub’s commitment to open development and responsiveness to community feedback, ensuring wider usability and engagement.

Infrahub and Kriten offer a comprehensive solution to streamline infrastructure automation across teams by integrating data management with execution capabilities. Infrahub serves as a centralized platform for storing and managing infrastructure data, utilizing a graph database to define customizable schemas and generating configuration artifacts in formats like Terraform, Ansible, and Kubernetes manifests. This data is version-controlled and validated, ensuring consistency and compliance. Kriten, an API-first automation engine, transforms scripts into secure REST API endpoints, allowing for reliable and scalable execution of tasks across different domains without requiring teams to master each tool. By using Infrahub to model infrastructure data and Kriten to handle task execution, organizations can automate workflows like network device configuration, multi-cloud provisioning, and GitOps for infrastructure, while maintaining robust security through features like RBAC, audit trails, and enterprise SSO. This integration not only facilitates cross-domain automation but also enhances governance, scalability, and operational efficiency, making it an ideal framework for modern DevOps practices.

Trust is a crucial factor in the successful adoption of automation systems, as it influences whether users feel confident and safe in utilizing these tools. While technically proficient systems might fail due to a lack of trust, this problem often arises from inadequate transparency, predictability, and user-friendliness, causing operators to revert to familiar manual processes. The disconnect between system builders, who understand the intricacies, and users, who may view the system as an unpredictable "black box," exacerbates this issue. Examples like Ansible highlight how default settings and the absence of intuitive safeguards can quickly erode trust when users encounter unexpected outcomes. To remedy this, trust must be an integral part of the system's design from the outset, requiring deliberate efforts to ensure that automation tools are not only technically sound but also transparent, manageable, and aligned with real-world user needs.

Trust is crucial for the successful adoption of infrastructure automation, as people are often hesitant to embrace tools they do not trust. A framework called the 6+3+3 model is designed to build trust by incorporating six core principles, three technical foundations, and three operational practices. The principles emphasize predictability, manageability, transparency, simplicity, reliability, and a human-friendly approach, all aimed at enhancing the user experience. The technical foundations—idempotency, dry runs, and transactional execution—support these principles by ensuring consistent and transparent operations. Operational practices such as version control, testing, and declarative automation help maintain trust as systems scale and evolve. Implementing this framework can prevent the common pitfalls of opaque, fragile, or unpredictable automation systems, ultimately leading to higher adoption rates and more reliable outcomes. Understanding and applying the 6+3+3 framework is not just the responsibility of developers but also of users, as it fosters better communication and alignment on what constitutes a trustworthy automation system.

Declarative automation is emerging as a more scalable, reliable, and trustworthy approach to modern infrastructure compared to imperative automation, which becomes cumbersome as complexity increases. While imperative automation encodes each action step-by-step, declarative automation focuses on defining the desired end state, allowing the system to determine and execute the necessary steps to achieve that state. This shift reduces complexity, enhances predictability, and supports idempotency, where re-applying a definition does not produce unintended changes. Declarative systems are inherently more resilient, capable of retrying or reconverging in the event of partial failures, which reduces the risk of inconsistent states. Tools like Terraform and Kubernetes exemplify how declarative models have become foundational in cloud and container orchestration, ensuring consistent outcomes and simplifying testing and maintenance. As infrastructure environments grow in scale and complexity, the adoption of declarative approaches—while not entirely replacing imperative methods—offers a more effective default strategy for automation by enhancing error handling, maintenance, and resilience.

Choosing the right infrastructure automation tools is crucial for building a successful automation system, as it involves selecting components that work together cohesively and foster trust within an organization. Success in infrastructure automation is not solely dependent on individual tools but on creating a stack where each component complements the others, ensuring no weak links hinder overall performance. The decision to build or buy automation tools should be guided by the principle of building only what is necessary and integrating existing solutions whenever possible, as demonstrated by OpsMill's integration of Prefect for improved workflow orchestration. Important characteristics of effective infrastructure automation tools include declarative automation, idempotency, version control, testing and validation capabilities, traceability and logging, developer experience, and programmable interfaces. Evaluating tools with a system designer's mindset, considering factors such as visibility, control, safety, and adaptability, ensures that each addition to the automation stack supports long-term success and aligns with trust-building goals.

The blog post provides a comprehensive guide on running integration tests in Infrahub, emphasizing the importance of verifying how different components work together in a real environment. It introduces tools like Docker and Testcontainers to create isolated Infrahub instances for testing purposes, allowing for the automation of schema and data loading to preemptively identify issues before they reach production. The post walks through setting up these tests without requiring extensive Python expertise, offering step-by-step instructions and example code, which can also be followed via video tutorials on Cisco DevNet's YouTube channel. It contrasts integration testing with unit testing, highlighting how the former ensures that multiple system components operate cohesively with real data, as opposed to unit tests that focus on individual functions in isolation. Additionally, it demonstrates using the infrahub-testcontainers Python library to manage the test environment, making it easier to write realistic and reliable tests by spinning up temporary Infrahub instances and cleaning them up post-testing. The guide also showcases how to write integration tests using pytest, including setting up schema and data directories, and explains the benefits of integrating local Git repositories directly into the test environment to streamline development processes.