InfoQ Homepage AI Architecture Content on InfoQ
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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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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.
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AI Changes the Data Interface, Not the Query Engine
This article presents a disaster-recovery analytics workflow in which an AI agent generates constrained SQL or Python, DuckDB computes reproducible facts from Parquet, and the agent interprets the results. It explains the determinism split, replayable analysis, and four signals for deciding when an embedded engine is sufficient or a managed lakehouse is justified.
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Article Series: Agentic AI Architecture
In this article series, we try to establish agentic AI architecture as a new type of software architecture that will likely dominate the industry for years to come. The articles, written by industry experts, cover various elements and aspects of agentic AI architecture. We aim to present the latest trends and developments shaping the new type of architecture as it enters the mainstream.
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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.
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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.
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Agentic Fitness Functions: Extending Evolutionary Architecture Beyond Deterministic Rules
Deterministic rules safeguard hard metrics, but what about architectural intent? Discover how agentic fitness functions combine AI agents and versioned rubrics to evaluate complex, judgment-heavy concerns—such as boundary fidelity, semantic contract drift, and stale ADR assumptions. Elevate evolutionary architecture governance with continuous, calibrated feedback loops.
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Multi-Agent AI for Production Security Operations: an A2A and MCP Architecture in a 5G Core
The bottleneck in a mature SOC is rarely analyst triage; rather, it is the detection-engineering team's ability to keep the rule base aligned with a threat landscape that evolves faster than rules can be written. Learn how multi-agent system for production security operations has reduced mean times to detect and to respond by 40% and compressed the human work required by 12x.
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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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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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Architecting Autonomy at Scale: Raising Teams without Creating Dependencies
Modern engineering needs a shift from "gates" to "guardrails." Scale via decentralized architecture that treats teams like adults—building judgment through Socratic coaching, shared platforms, and automated drift detection. Move beyond bottlenecks to an interdependent model where AI governance and ADRs preserve context without killing velocity. Empower autonomy while maintaining alignment.
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Architectural Governance at AI Speed
In the GenAI era, code is a commodity, but alignment is not. Traditional review boards can't scale with AI-generated output. This article explores "Declarative Architecture" - transforming ADRs and Event Models into automated guardrails. Move beyond "dumping left" to a model where the conformant path is the path of least resistance, enabling decentralized governance without losing cohesion.