InfoQ Homepage Programming Content on InfoQ
-
/articles/agent-assisted-intelligent-observability/en/smallimage/agent-assisted-intelligent-thumbnail-1769595476571.jpg)
From Alert Fatigue to Agent-Assisted Intelligent Observability
As systems grow, observability becomes harder to maintain and incidents harder to diagnose. Agentic observability layers AI on existing tools, starting in read-only mode to detect anomalies and summarize issues. Over time, agents add context, correlate signals, and automate low-risk tasks. This approach frees engineers to focus on analysis and judgment.
-
/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/why-ml-projects-fail-production/en/smallimage/why-ml-projects-fail-production-thumbnail-1769072877092.jpg)
Why Most Machine Learning Projects Fail to Reach Production
In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.
-
/articles/ai-developers-rewriting-software-process/en/smallimage/ai-developers-rewriting-software-process-thumbnail-1768398859653.jpg)
Virtual Panel - AI in the Trenches: How Developers Are Rewriting the Software Process
This virtual panel brings together engineers, architects, and technical leaders to explore how AI is changing the landscape of software development. Practitioners share their insights on successes and failures when AI is incorporated into daily workflows, emphasizing the significance of context, validation, and cultural adaptation in making AI a sustainable element of modern engineering practices.
-
/articles/ai-assisted-development-series/en/smallimage/ai-assisted-development-thumb-image-1768388299998.jpg)
Article Series: AI-Assisted Development: Real World Patterns, Pitfalls, and Production Readiness
In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline. As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. This transition is redefining what constitutes good software engineering.
-
/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/agentic-terminal-cli-agents/en/smallimage/agentic-terminal-cli-agents-thumbnail-1767616966806.jpg)
Agentic Terminal - How Your Terminal Comes Alive with CLI Agents
In this article author Sachin Joglekar discusses the transformation of CLI terminals becoming agentic where developers can state goals while the AI agents plan, call tools, iterate, ask for approval where needed, and execute the requests. He also explains the planning styles for three different CLI tools: Gemini, Claude, and Auto-GPT.
-
/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/nextgen-search-ai-opensearch-mcp/en/smallimage/nextgen-search-ai-thumbnail-1765802308459.jpg)
NextGen Search - Where AI Meets OpenSearch through MCP
In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP).
-
/articles/java-trends-report-2025/en/smallimage/the-infoq-trends-report-500x500-article-thumb-logo-1764844623172.jpeg)
InfoQ Java Trends Report 2025
This report summarizes how the InfoQ Java editorial team and several Java Champions currently see the adoption of technology and emerging trends within the Java and JVM space in 2025. We focus on Java the language, as well as related languages like Kotlin and Scala, the Java Virtual Machine (JVM), and Java-based frameworks and utilities.
-
/articles/reducing-false-positives-retrieval-augmented-generation/en/smallimage/reducing-false-positives-retrieval-augmented-generation-thumbnail-1762786576150.jpg)
Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study
In this article, author Elakkiya Daivam discusses why Retrieval Augmented Generation (RAG) and semantic caching techniques are powerful levers for reducing false positives in AI powered applications. She shares the insights from a production-grade evaluation with 1,000 query variations tested across seven bi-encoder models.
-
/articles/training-data-preprocessing-for-text-to-video-models/en/smallimage/training-data-preprocessing-for-text-to-video-models-thumbnail-1762244675850.jpg)
Training Data Preprocessing for Text-to-Video Models
In this article, author Aleksandr Rezanov discusses the data preparation for generative text-to-image models to accelerate work on video generation services to be used in TV series and films. He explains how data is prepared and can serve as a starting point for creating custom datasets to develop proprietary models.