InfoQ Homepage Architecture Content on InfoQ
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/articles/beyond-rag-context-aware/en/smallimage/beyond-rag-context-aware-thumbnail-1774531119239.jpg)
Beyond RAG: Architecting Context-Aware AI Systems with Spring Boot
This article introduces Context-Augmented Generation (CAG) as an architectural refinement of RAG for enterprise systems. It shows how a Spring Boot-based context manager can incorporate user identity, session state, and policy constraints into AI workflows, improving traceability, consistency, and governance without altering existing retrievers or LLM infrastructure.
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Event-Driven Patterns for Cloud-Native Banking: Lessons from What Works and What Hurts
Event-driven architecture helps banks decouple systems, scale services, and create clear activity trails. But it also introduces complexity, new failure modes, and operational challenges. Chris Tacey-Green explains where it adds value in banking systems and the practical patterns, such as inbox/outbox and stable event contracts, needed to make it reliable.
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Architecting Autonomy at Scale: Raising Teams without Creating Dependencies
Modern engineering needs a shift from "gates" to "guardrails." Scale via decentralized architecture that treats teams like adults—building judgment through Socratic coaching, shared platforms, and automated drift detection. Move beyond bottlenecks to an interdependent model where AI governance and ADRs preserve context without killing velocity. Empower autonomy while maintaining alignment.
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/articles/architectural-governance-ai-speed/en/smallimage/architectural-governance-ai-speed-thumbnail-1773997111820.jpg)
Architectural Governance at AI Speed
In the GenAI era, code is a commodity, but alignment is not. Traditional review boards can't scale with AI-generated output. This article explores "Declarative Architecture" - transforming ADRs and Event Models into automated guardrails. Move beyond "dumping left" to a model where the conformant path is the path of least resistance, enabling decentralized governance without losing cohesion.
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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/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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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/kotlin-scalable-swiftui-patterns/en/smallimage/thumbnail-kotlin-scalable-swiftui-patterns-1771922819056.jpg)
Borrowing from Kotlin/Android to Architect Scalable iOS Apps in SwiftUI
Building iOS apps can feel like stitching together guidance from blog posts and Apple samples, which are rarely representative of how production architectures grow and survive. In contrast, the Kotlin/Android ecosystem has converged on well-documented, real-world patterns. This article explores how those approaches can be translated into Swift/SwiftUI to create maintainable, scalable iOS apps.
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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/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.