InfoQ Homepage Development Content on InfoQ
-
/articles/next-dsl-author-language-model/en/smallimage/Your-Next-DSL-Author-Is-a-Language-Model-thumb-1789048983233.jpg)
Your Next DSL Author is a Language Model
In this article, the author introduces Typed Domain Grounding, an approach to reducing LLM hallucinations in domain-specific languages by embedding them in mainstream typed languages. Using kUML benchmarks and an infrastructure-as-code example, he explores how compiler validation and generate-compile-repair loops can make model-generated DSL output more reliable.
-
/articles/chaos-engineering-ecs-payments/en/smallimage/chaos-engineering-ecs-payments-thumbnail-1788254676411.jpg)
Implementing Chaos Engineering in Financial Payment Systems: Lessons from Enterprise ECS Deployments
Standard chaos engineering assumes experiments stop cleanly, blast radius is knowable in advance, and production is fair game. Payment systems violate all three. Salim Adedeji describes ECS-specific failure modes from enterprise deployments: a 60-second DNS TTL that produced 93-second failover, retry logic amplifying database load 2.4x, and AZ rebalancing loops that generic tooling misses.
-
/articles/pqc-in-spring-boot/en/smallimage/pqc-in-spring-boot-thumbnail-1787646727709.jpg)
Post-Quantum Cryptography in Spring Boot: Four Patterns You Can Ship This Sprint
There are four patterns that bring PQC into a Spring Boot fleet: encrypting payloads between services, locking down database fields, signing documents that need to hold up for decades, and moving service tokens off RS256. Along the way, we discuss why Harvest Now, Decrypt Later is already happening, and why none of this is production-safe until KMS or Vault is in place.
-
/articles/agentic-fitness-functions-evolutionary-architecture/en/smallimage/thumbnail-1786428822427.jpg)
Agentic Fitness Functions: Extending Evolutionary Architecture Beyond Deterministic Rules
Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops.
-
/articles/system-comprehension-evolutionary-architecture/en/smallimage/thumbnail-1786087830832.jpg)
Comprehension as an Architectural Characteristic: A System That Is Not Understood Cannot Evolve Safely
As AI commoditizes code output, system comprehension silently decays, creating cognitive debt that threatens safe architectural evolution. This article explores why human understanding must be treated as an essential architectural characteristic, offering actionable strategies, socio-technical metrics, and design checkpoints to preserve intent across modern engineering teams.
-
/articles/evolutionary-architecture-change-locality/en/smallimage/evolutionary-architecture-change-locality-thumbnail-1785239081753.jpg)
Enabling Evolutionary Architecture through the Preservation of Change Locality
Why do simple features suddenly require cross-team negotiations? In this article, explore how boundary drift quietly destroys change locality and increases cognitive load across teams. Learn practical sociotechnical strategies - redistributing mechanics, exposing essential policy, and rehearsing exception paths - to restore domain boundaries and enable a truly evolutionary software architecture.
-
/articles/virtual-threads-after-jdk24/en/smallimage/virtual-threads-after-jdk24-thumbnail-1785232507934.jpg)
Virtual Threads after JDK 24: What Changed for Production Java
JDK 24 removed the monitor-related carrier-thread pinning that stalled Netflix and similar teams on Java 21. What has replaced it on JDK 25 LTS is downstream-resource saturation: the bottleneck moved and now demands explicit bounding in application code. This article maps the failure modes that surface after virtual-thread adoption and gives a practical sequence backed by a public benchmark.
-
/articles/securing-mcp-production-gateway/en/smallimage/securing-mcp-production-gateway-thumbnail-1784884301110.jpg)
Securing MCP in Production: Defense-in-Depth beyond the Gateway
This article presents a defense-in-depth approach for securing Model Context Protocol (MCP) deployments in production. It outlines four architectural control layers: safe execution, management infrastructure, outbound trust, and semantic integrity, arguing that production security requires enforcement beyond the gateway at the earliest trustworthy control points.
-
/articles/evolutionary-architecture-pattern/en/smallimage/evolutionary-architecture-pattern-thumbnail-1784798616611.jpg)
An Evolutionary Architecture Pattern for Managing AI’s Pace of Change
Traditional API gateways assume deterministic services and simple schemas - assumptions agentic AI breaks. Discover why enterprise engineering leaders are adopting AI Gateways as an evolutionary architecture seam. Centralize guardrails, model routing, agent identity, action policy, and semantic audit within a single control plane to prevent costly incidents while keeping core platforms stable.
-
/articles/self-building-agent-langchain4j/en/smallimage/the-self-building-agent-a-langChain4j-experiment-thumb-1784637877074.jpg)
The Self-Building Agent: a LangChain4j Experiment
The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autonomously. Results showed that two architectural patterns—supervisor and workflow—offered different trade-offs between flexibility and execution speed during debugging tasks.
-
/articles/ai-speed-context-store-architecture/en/smallimage/ai-speed-context-store-architecture-thumbnail-1783673492911.jpg)
Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture
AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely.
-
/articles/aws-multi-region-signing/en/smallimage/aws-multi-region-signing-thumbnail-1783602485181.jpg)
Removing a Hidden Round Trip from a Multi-Region AWS API
When a series of regional outages forced a rethink of a multi-region AWS API, the team discovered that an obstacle to global failover was hiding in plain sight: a pre-flight discovery call baked into every client session years earlier as the only available option. This article describes what it took to remove it, and what the rollout actually cost.