InfoQ Homepage Artificial Intelligence Content on InfoQ
-
/articles/distributed-systems-microservices-agentic-ai/en/smallimage/thumbnail-1792138504151.jpg)
From Microservices to Agents: the Next Evolution of Distributed Systems
The article examines the transition from microservices to agentic AI architecture in distributed systems. Microservices allow for functional decomposition, while agentic systems focus on decision decomposition. This shift introduces new complexities in observability, reliability, and system design, highlighting the need for updated architectural patterns and practices.
-
/articles/platform-engineering-playbook-production-llms/en/smallimage/thumbnail-1790763235683.jpg)
The Platform Engineering Playbook for Production LLMs
In this article, author discusses his experience with AI agent hallucinations in an inventory recommendation system and how this problem was solved by treating the LLM stack as a platform infrastructure concern instead of as an application one. He makes a case for a shared LLM platform with common services like prompt registry & versioning, schema enforcement and token cost attribution by request.
-
/articles/architecture-enterprise-personalization-relevance-governance/en/smallimage/Beyond-Relevance-A-Governance-First-Architecture-for-Enterprise-Personalization-thumb-1789730126317.jpg)
Beyond Relevance: a Governance-First Architecture for Enterprise Personalization
This article examines the limitations of conventional personalization systems and highlights the need for a governance-first architecture. It emphasizes separating relevance from governance to ensure recommendations are context-aware, auditable, and compliant. The architecture integrates stateful memory, policy-driven AI orchestration, and explainable scoring to enhance personalization systems.
-
/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/when-spec-driven-development-pays-off/en/smallimage/When-Spec-Driven-Development-Pays-Off-thumb-1788790605567.jpg)
When Spec-Driven Development Pays off
AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent.
-
/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/culture-trends-2026/en/smallimage/infoq-trends-report-small-logo-1784644382346.jpeg)
InfoQ Culture and Methods Trends Report - 2026
This report summarizes how the InfoQ Culture and Methods editorial team sees the ongoing and emergent trends in the culture and methods space in 2026.
-
/articles/ai-workflow-pattern/en/smallimage/thumbnail-1785766770921.jpg)
Runtime-Agnostic AI Workflows: a Pattern for Production Durability and Fast Eval Iteration
AI workflows have two needs that trade off directly. Running reliably in production requires persisting and distributing every step so it survives crashes, deploys, and restarts. But that same machinery is what makes runs too heavy for the fast, throwaway loop you need to check an LLM's output quality. The properties that buy durability are the ones that kill iteration speed.
-
/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.