InfoQ Homepage Artificial Intelligence Content on InfoQ
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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.
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Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution
This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows.
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Understanding ML Model Poisoning: How it Happens and How to Detect it
In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines.
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Governing AI in the Cloud: a Practical Guide for Architects
In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes.
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Artificial Intelligence-Driven Phishing: How Phishing Technique Is Evolving and Implemented
In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness.
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The Technology Adoption Curve, Twenty Years On
Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.
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Article Series: Securing the AI Stack: from Model to Production
This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance.
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Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG
In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution.
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The AI Productivity Paradox in Test Automation: Moving beyond Structural Validation to Perception and Intent
The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows that to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent.
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/articles/local-first-ai-inference-cloud/en/smallimage/Local-First-AI-Inference-A-Cloud-Architecture-Pattern-for-Cost-Effective-Document-Processing-thumb-image-1778141518292.jpg)
Local-First AI Inference: a Cloud Architecture Pattern for Cost-Effective Document Processing
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
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/articles/mcp-java-architectural-strategy-llm-integrations/en/smallimage/mcp-java-architectural-strategy-llm-integrations-thumbnail-1776772947180.jpg)
MCP in the Java World: Bringing Architectural Strategy to LLM Integrations
Discover how the Model Context Protocol (MCP) Java SDK is establishing a new architectural discipline for enterprise LLM integrations. By defining explicit contracts and leveraging MCP servers as anti-corruption layers, it ensures governance, loose coupling, and security alignment with the JVM ecosystem and existing operational practices, moving integrations beyond fragility to resilience.
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Orchestrating Agentic and Multimodal AI Pipelines with Apache Camel
In this article, author Vignesh Durai discusses how agentic and multimodal AI systems can be engineered using Apache Camel and LangChain4j technologies. The key components in the solution include LLM-based reasoning, retrieval-augmented generation (RAG), and image classification.