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Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures
The article argues that reliable agentic AI architecture depends on a context engine that selects, injects, compresses, and expires information before it reaches the model. It separates context, memory, and knowledge, and recommends tiered memory, curated knowledge, and token budgeting. It also covers common anti-patterns, such as context stuffing and stale data loops, with mitigations.
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InfoQ AI, ML and Data Engineering Trends Report - 2026
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.
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Five Ways to Use AI Coding Agents to Improve Your Software Architecture
Modern architectures often use legacy services for specific tasks, but these services may lack accurate documentation and using them can be risky if you don't understand them well. AI coding agents can help close that knowledge gap.
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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.
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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.
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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% .