Autonomous AI Agent Architectures: A Comprehensive Survey of Design Patterns, Reasoning Frameworks, and Deployment Strategies

The emergence of large language models (LLMs) as cognitive backbones hascatalysed a paradigm shift in the design of autonomous AI agents. These agents gobeyond traditional rule-based or reinforcement-learning-based systems by integratingplanning, tool use, memory, and multi-agent collaboration into unied architectures.This survey provides a comprehensive review of over 150 recent works on autonomousAI agent architectures, organized along three axes: (i) design patterns, includingsingle-agent, multi-agent, and hierarchical congurations; (ii) reasoning frameworkssuch as chain-of-thought prompting, tree-of-thought search, and reexion-based selfimprovement; and (iii) deployment strategies encompassing sandboxed execution,human-in-the-loop oversight, and distributed agent swarms. We propose a noveltaxonomy that unies existing classication schemes and present comparative evaluations on standard benchmarks including WebArena, SWE-bench, and GAIA.Our analysis identies key open challengesrobustness, alignment, scalability, andveriabilityand charts promising research directions for the next generation ofautonomous AI agents.