InfoQ Homepage Architecture & Design Content on InfoQ
-
/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.
-
/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.
-
/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.
-
/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.
-
/articles/engineering-speed-scale/en/smallimage/thumbnail-article-1767006608903.jpg)
Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs
Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.
-
/articles/durable-objects-handle-inflight-requests/en/smallimage/durable-objects-handle-inflight-requests-thumbnail-1769161248259.jpg)
One Cache to Rule Them All: Handling Responses and In-Flight Requests with Durable Objects
Traditional caching fails to stop "thundering herds" where multiple clients trigger the same work during a miss. This article proposes using Cloudflare Durable Objects to treat in-flight work and finished results as two states of one cache entry. By routing to a single owner, systems eliminate redundant tasks. This pattern replaces complex locks with simple promises, simplifying the system design.
-
/articles/preventing-data-exfiltration-google-cloud/en/smallimage/preventing-data-exfiltration-google-cloud-thumbnail-1768221099670.jpg)
Preventing Data Exfiltration: a Practical Implementation of VPC Service Controls at Enterprise Scale in Google Cloud Platform
Implementing VPC Service Controls is more about people and process than technology. Organizations must conduct extensive upfront discovery, use phased rollouts to avoid breaking production systems, and design VPC Service Controls that enable rather than block work. Success requires automation, clear exception processes, tracking both security and business metrics, and continuous improvement.
-
/articles/spec-driven-development/en/smallimage/spec-driven-development-thumbnail-1767777707872.jpg)
Spec Driven Development: When Architecture Becomes Executable
Spec-Driven Development inverts traditional architecture by making specifications executable and authoritative. It transforms declared intent into validated code through AI generation and provides architectural determinism. It eliminates drift through continuous enforcement, but demands new engineering discipline in schema design and contract-first reasoning.
-
/articles/architects-dilemma/en/smallimage/architect-dillema-choose-path-or-pave-your-own-way-thumb-1767021546327.jpg)
The Architect’s Dilemma: Choose a Proven Path or Pave Your Own Way?
Software platforms and frameworks act like paved roads: they accelerate MVP/MVA delivery but impose decisions teams may not accept. If the paved roads don't reach your destination, then you may have to take an exit ramp and build your own solution. Experiments are necessary to determine which path meets your specific needs.
-
/articles/architects-ai-era/en/smallimage/Where-Architects-Sit-in-the-Era-of-AI-thumb-image-1765888921801.jpg)
Where Architects Sit in the Era of AI
As AI evolves from tool to collaborator, architects must shift from manual design to meta-design. This article introduces the "Three Loops" framework (In, On, Out) to help navigate this transition. It explores how to balance oversight with delegation, mitigate risks like skill atrophy, and design the governance structures that keep AI-augmented systems safe and aligned with human intent.
-
/articles/architecture-ai-augmented-change/en/smallimage/Architecture-in-a-Flow-of-AI-Augmented-Change-thumb-image-1765883092263.jpg)
Architecture in a Flow of AI-Augmented Change
While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change.
-
/articles/secure-ai-development/en/smallimage/thumbnail-1765541260371.jpg)
Trustworthy Productivity: Securing AI Accelerated Development
Autonomous AI agents amplify productivity but can cause severe damage without safeguards. Defend the ReAct loop—context, reasoning, and tools—through provenance gates, planner-critic separation, scoped credentials, sandboxed code, and STRIDE/MAESTRO threat modeling. With robust logging, bounded autonomy, and red-teaming, agents can deliver trustworthy productivity while minimizing risk.