InfoQ Homepage Architecture & Design Content on InfoQ
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/articles/portable-systems-sovereignty/en/smallimage/Architecting-Portable-Systems-on-Open-Standards-for-Digital-Sovereignty-thumb-1773829386100.jpg)
Architecting Portable Systems on Open Standards for Digital Sovereignty
Digital sovereignty is about maintaining control of critical systems by limiting reliance on any single vendor. Open standards and portable architectures reduce lock‑in and keep migration options open, even when providers change pricing, licensing, or viability. Full independence is impossible, but disciplined design and clear guardrails strengthen resilience.
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/articles/configuration-control-plane/en/smallimage/configuration-as-a-control-plane-designing-for-safety-and-reliability-at-scale-thumb-1773657574566.jpg)
Configuration as a Control Plane: Designing for Safety and Reliability at Scale
Configuration has evolved from static deployment files into a live control plane that directly shapes system behavior. The evolution of configuration management highlights why misconfigurations can trigger large outages and how hyperscalers deploy changes safely using staged rollouts, validation, blast radius limits, and automated rollback at scale.
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/articles/practical-robustness-going-beyond-memory-safety-rust/en/smallimage/beyond-memory-safety-what-makes-rust-different-lessons-from-autonomous-robotics-thumb-1773646654979.jpg)
Beyond Memory Safety: What Makes Rust Different – Lessons from Autonomous Robotics
This article explores that question through the lens of a real-world Rust project: a system responsible for controlling fleets of autonomous mobile robots. While Rust's memory safety is a strong foundation, its true power lies in the type system and ownership rules. The session will go beyond memory safety and explore ways to encode behavior and protocols directly into types.
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/articles/evaluating-ai-agents-lessons-learned/en/smallimage/evaluating-ai-agents-lessons-learned-thumbnail-1773307288872.jpg)
Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned
This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
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/articles/oil-water-moment-ai-architecture/en/smallimage/the-oil-and-water-moment-in-ai-architecture-thumbnail-1773149586690.jpg)
The Oil and Water Moment in AI Architecture
Have you ever tried mixing oil and water? That is the moment software architecture is entering as deterministic systems meet non deterministic AI behaviour. Architects must anchor intelligent systems in intent, governance and systems thinking. This article introduces the Architect’s V Impact Canvas, a framework for navigating this shift while keeping human trust at the centre.
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/articles/change-metrics-system-reliability/en/smallimage/change-metrics-systems-reliability-thumbnail-1772787617464.jpg)
Change as Metrics: Measuring System Reliability through Change Delivery Signals
System changes are the primary driver of production incidents, making change-related metrics essential reliability signals. A minimal metric set of Change Lead Time, Change Success Rate, and Incident Leakage Rate assesses delivery efficiency and reliability, supported by actionable technical metrics and an event-centric data warehouse for unified change observability.
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/articles/read-copy-update/en/smallimage/read-copy-update-thumbnail-1772618412119.jpg)
Read-Copy-Update (RCU): the Secret to Lock-Free Performance
Innovative software engineer with expertise in optimizing concurrency through advanced techniques like Read-Copy-Update (RCU). Proven track record of boosting read performance by over 110% in read-heavy workloads. Skilled in leveraging RCU principles across production systems, enhancing architecture efficiency, and streamlining data handling to maximize scalability and minimize overhead.
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/articles/enterprise-spec-driven-development/en/smallimage/enterprise-spec-driven-development-thumbnail-1771319821996.jpg)
Spec-Driven Development – Adoption at Enterprise Scale
Spec‑Driven Development shifts AI‑augmented software delivery from tactical prompting to collaborative intent articulation. Enterprises face gaps in tooling, workflow integration, multi‑repo coordination, and cross‑functional collaboration. Sustainable adoption requires treating specs as living, shared interfaces, and evolving organizational practices.
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/articles/proactive-autoscaling-edge-kubernetes/en/smallimage/proactive-autoscaling-edge-kubernetes-thumbnail-1770721062673.jpg)
Proactive Autoscaling for Edge Applications in Kubernetes
Kubernetes often reacts too late when traffic suddenly increases at the edge. A proactive scaling approach that considers response time, spare CPU capacity, and container startup delays can add or remove instances more smoothly, prevent sudden spikes, and keep performance stable on systems with limited resources.
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/articles/ai-generated-mvp/en/smallimage/thumbnail-ai-generated-mvp-1770282822570.jpg)
You’ve Generated Your MVP Using AI. What Does That Mean for Your Software Architecture?
AI‑generated code creates implicit architectural decisions, forcing teams to rely on experimentation to validate quality attributes. To get useful results from AI, teams must clearly express trade‑offs and reasoning so the model can generate solutions aligned with desired QARs.
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/articles/jakartaee-12-milestone-2/en/smallimage/jakartaee-12-milestone-2-thumbnail-1770367607817.jpg)
Jakarta EE 12 Milestone 2: Advent of the Data Age along with Consistency and Configuration
Jakarta EE 12 Milestone 2 marks the beginning of the next generation of enterprise Java. It introduces Jakarta Query, a unified query language across Persistence, Data, and NoSQL, while aligning the platform with Java 21. This milestone focuses on integration, modernization, and improving developer productivity for cloud-native enterprise applications.
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/articles/skeleton-architecture/en/smallimage/skeleton-architecture-thumbnail-1769508521191.jpg)
Working with Code Assistants: the Skeleton Architecture
Prevent AI-generated tech debt with Skeleton Architecture. This approach separates human-governed infrastructure (Skeleton) from AI-generated logic (Tissue) using Vertical Slices and Dependency Inversion. By enforcing security and flow control in rigid base classes, you constrain the AI to safe boundaries, enabling high velocity without compromising system integrity.