AI Architecture

Arunkumar (2026)

AI systems are evolving from text generators to autonomous agents, but the architecture for making them reliable is still being invented

A comprehensive survey proposes a six-dimension taxonomy for LLM-based agents, mapping the shift from simple reasoning loops to hierarchical multi-agent systems with standardized tool connectivity, and catalogues the open failure modes that still block real-world deployment.

6 Dimensions

unified taxonomy: Core Components, Cognitive Architecture, Learning, Multi-Agent Systems, Environments, and Evaluation

3 Topologies

multi-agent collaboration patterns identified: chain (waterfall), star (hub-and-spoke), and mesh (swarm)

Alenezi (2026)

AI agents are no longer just answering prompts, they're becoming goal-directed systems with their own control loops

This paper maps the architectural shift from stateless LLM calls to autonomous agent systems with typed tools, hierarchical memory, multi-agent coordination, and governance baked in from the start.

Reference Architecture

a layered stack that separates LLM cognition from control flow, memory, tool execution, and cross-cutting governance

4 Multi-Agent Topologies

orchestrator-worker, router-solver, hierarchical command, and swarm, each with mapped failure modes and mitigations

Vandeputte (2025)

Stop letting AI agents run everything; make them automate themselves out of the critical path

A Nokia Bell Labs framework argues that reliable GenAI systems should blend traditional software engineering with cognitive AI processing, keeping agents as occasional problem-solvers rather than permanent gatekeepers.

Su et al. (2025)

Kubernetes dominates five years of practitioner talks; while planning and coding get almost no attention

An analysis of 5,677 talks from eight major industry conferences (2020 - 2024) reveals that a tiny cluster of technologies shapes modern software architecture, most tools serve late DevOps stages, and early design phases remain a blind spot.

5,677

Practitioner talks analysed across 8 major industry conferences (2020 - 2024)

1,054

Mentions for Kubernetes, the single most referenced technology out of 450 identified

Esposito et al. (2025)

GenAI can help architects sketch systems; but nobody is checking whether the sketches are right

A multivocal literature review of 46 studies finds GenAI is already embedded in early architectural tasks, yet 93% of the work skips formal validation of what the models produce.

62%

Of LLM usage across reviewed studies relied on OpenAI GPT models

85%

Of studies involved human interaction with the model, confirming an assistive (not autonomous) role