InfoQ Homepage Large language models Content on InfoQ
-
/articles/llm-reliability-layers-limits-accuracy/en/smallimage/llm-reliability-layers-limits-accuracy-thumbnail-1793873839325.jpg)
LLM Reliability Layers Under Test: Accuracy, Yield, and the Limits of Verification
Verification layers sort outputs by trust rather than fixing errors. In a benchmark of six reliability layers, voting and self-correction failed to recover accuracy - they only withheld doubtful records. Production systems gain more reliability by replacing generation with deterministic lookups, tracking yield over raw accuracy, and resolving extraction bottlenecks before stacking checks.
-
/articles/agentic-ai-architecture-challenges-opportunities/en/smallimage/agentic-ai-architecture-challenges-opportunities-thumbnail-1793709540690.jpg)
Agentic AI Architecture: Current Challenges and Future Opportunities
Agentic AI architecture extends microservices and cloud-native architectures with AI agents that work with context, memory, and protocols such as MCP and A2A. The article covers bounded-context integration, task-dependent coordination strategies, new security risks, and observability needs. It expects gradual adoption alongside existing architectures, with platforms absorbing common components.
-
/articles/agentic-ai-context-memory-knowledge/en/smallimage/agentic-ai-context-memory-knowledge-thumbnail-1793173492489.jpg)
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.
-
/articles/ai-ml-data-engineering-trends-2026/en/smallimage/the-infoq-trends-report-500x500-logo-1792658101354.jpeg)
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.
-
/articles/agentic-ai-architecture-harness-evolution/en/smallimage/agentic-ai-architecture-harness-evolution-thumbnail-1792744961862.jpg)
The Evolution of Agentic Harnesses: from Chains to Graph to Code
Agentic LLM systems have moved from linear prompt chains to graph-based workflows and, more recently, code-executing harnesses. Chains are simple but fragile, graphs add branching, retries, and validation, and code harnesses verify outputs through execution. The article advises validating model outputs and choosing the least complex design that meets reliability, speed, and cost needs.
-
/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/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/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.