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
InfoQ Homepage Articles Article Series: Agentic AI Architecture
These new systems will harness the power of AI in ways that, even today, are perhaps not fully comprehensible, as this space is moving forward at a neck-breaking pace, and last year’s best practice is this year’s antipattern. This rapid pace of innovation comes with many challenges, but can also be exciting for those willing to join the ride.
In this 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.
While it’s impossible to characterize the new type of software architecture fully at this point, we hope this series offers useful insights into how the agentic AI space will shape the future of the IT industry in the coming years. Agentic AI Architecture is still in its infancy, but we hope to track how it evolves and matures and cover all key milestones and breakthroughs in the future.
You can download the entire series collated in PDF format, in the associated eMag.
This article discusses the shift in distributed systems architecture, driven by large language models (LLMs) and reasoning systems. Mallika Rao argues that agentic AI architectures focus on decomposing decisions, much like microservice architectures decompose functionality. Additionally, the author shares initial architectural patterns and failure modes introduced by the new paradigm, highlighting the increased importance of observability and reliability in these new types of agentic systems.
Karthik Ramgopal describes how building agents that interact with large language models has evolved in recent years from ad hoc experiments to resilient, production-grade architectures. Karthik also shares best practices and battle-tested advice for robust agentic AI architectures, drawn from his experience building several agentic solutions.
Adi Polak delves into the knowledge layer of agentic AI and introduces context engineering as a new discipline that has emerged in the agentic AI space. Adi argues that context management should be tuned to the system it supports and discusses key elements and mechanisms for incorporating high-quality context data into LLM prompts to reduce or eliminate hallucinations.
In this article, Subash Natarajan and Ahilan Ponnusamy describe a three-tier framework for designing and delivering agentic AI systems, covering many distinct elements, solutions, and patterns. The authors then offer industry-specific examples and share implementation strategies and guiding principles required for successful adoption.
Article by: Subash Natarajan and Ahilan Ponnusamy
Finally, Rafał Gancarz wraps up the eMag's overarching topic by examining how agentic AI architecture, as a new generation of software architecture, will change the IT industry as we know it today. Rafał connects the dots and covers a broad range of areas and architectural aspects that will need to adapt to address the challenges posed by agentic AI.
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