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Java

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.

Matteo Rossi
on Apr 27, 2026
Java

The Spring Team on Spring Framework 7 and Spring Boot 4

InfoQ recently spoke with key members of the Spring team about the significant architectural and functional advancements in Spring Framework 7 and Spring Boot 4. This conversation explores the strategic shift toward core resilience by integrating features such as retry and concurrency throttling directly into the framework, alongside the performance benefits of modularizing auto-configurations.

Karsten Silz Phil Webb Sam Brannen Rossen Stoyanchev Mark Pollack Martin Lippert Michael Minella
on Apr 13, 2026
Development

Stateful Continuation for AI Agents: Why Transport Layers Now Matter

Agent workflows make transport a first-order concern. Multi-turn, tool-heavy loops amplify overhead that is negligible in single-turn LLM use. Stateful continuation cuts overhead dramatically. Caching context server-side can reduce client-sent data by 80%+ and improve execution time by 15–29% .

Anirudh Mendiratta
on Apr 08, 2026
AI, ML & Data Engineering

Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners

This article presents a least-privilege AI Agent Gateway that places clear controls between AI agents and infrastructure. Agents do not access infrastructure APIs directly. Instead, every request is validated, authorized using policy as code with Open Policy Agent (OPA), and executed in short-lived, isolated environments, with built-in observability using OpenTelemetry.

Nabin Debnath
on Feb 23, 2026
Architecture & Design

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.

Pierre Pureur Kurt Bittner
on Feb 12, 2026
AI, ML & Data Engineering

Building LLMs in Resource-Constrained Environments: a Hands-On Perspective

In this article, the author argues that infrastructure and compute limitations can drive innovation. It demonstrates how smaller, efficient models, synthetic data generation, and disciplined engineering enable the creation of impactful LLM-based AI systems despite severe resource constraints.

Olimpiu Pop
on Feb 09, 2026
Development

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.

Patrick Farry
on Feb 03, 2026
AI, ML & Data Engineering

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.

Wenjie Zi
on Feb 02, 2026
AI, ML & Data Engineering

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.

Arthur Casals Mariia Bulycheva May Walter Phil Calçado Andreas Kollegger
on Jan 26, 2026
Architecture & Design

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.

Leigh Griffin Ray Carroll
on Jan 12, 2026
AI, ML & Data Engineering

Elevate Developer Experience with Generative AI Capabilities on AWS

This is a summary of a talk I gave at InfoQ Dev Summit Munich 2024. I discussed the transformative potential of generative AI in enhancing developer experiences, particularly through AWS. I’ll introduce key tools like Amazon Bedrock, Code Review Assistant, Agentic Code Generation, and Code Summarization in this article.

Olalekan Elesin
on Jan 27, 2025
Culture & Methods

Using Generative AI in Software Project Management to Bridge Domains and Accelerate Productivity

Gen AI Assistants play to the strengths of professionals with a breadth of experience, particularly software developers who can describe what they want the LLM to complete and critically evaluate the result. These tools enable us to swiftly cross divides of domain language and scale large repetitive tasks down to interesting ones on a human scale.

Ken Judy
on Jun 19, 2024

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