Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents

• URL: http://arxiv.org/abs/2311.13148v3
• Date: Wed, 3 Apr 2024 03:13:38 GMT
• Title: Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents
• Authors: Qinghua Lu, Liming Zhu, Xiwei Xu, Zhenchang Xing, Stefan Harrer, Jon Whittle,
• Abstract summary: Foundation model based agents derive their autonomy from the capabilities of foundation models. This paper presents a pattern-oriented reference architecture that serves as guidance when designing foundation model based agents.
• Score: 28.406492378232695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities. Foundation model based agents derive their autonomy from the capabilities of foundation models, which enable them to autonomously break down a given goal into a set of manageable tasks and orchestrate task execution to meet the goal. Despite the huge efforts put into building foundation model based agents, the architecture design of the agents has not yet been systematically explored. Also, while there are significant benefits of using agents for planning and execution, there are serious considerations regarding responsible AI related software quality attributes, such as security and accountability. Therefore, this paper presents a pattern-oriented reference architecture that serves as guidance when designing foundation model based agents. We evaluate the completeness and utility of the proposed reference architecture by mapping it to the architecture of two real-world agents.

Related papers

• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• LLM-Agent-UMF: LLM-based Agent Unified Modeling Framework for Seamless Integration of Multi Active/Passive Core-Agents [0.0]
  We propose a novel LLM-based Agent Unified Modeling Framework (LLM-Agent-UMF) Our framework distinguishes between the different components of an LLM-based agent, setting LLMs and tools apart from a new element, the core-agent. We evaluate our framework by applying it to thirteen state-of-the-art agents, thereby demonstrating its alignment with their functionalities.
  arXiv  Detail & Related papers  (2024-09-17T17:54:17Z)
• A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model [25.78239568393706]
  This paper introduces a taxonomy focused on the architectures of foundation-model-based agents. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents.
  arXiv  Detail & Related papers  (2024-08-06T03:10:52Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents [22.94671478021277]
  Foundation model-enabled generative artificial intelligence facilitates the development and implementation of agents. There is a lack of systematic knowledge to guide practitioners in designing the agents. We present a pattern catalogue consisting of 18 architectural patterns with analyses of the context, forces, and trade-offs.
  arXiv  Detail & Related papers  (2024-05-16T23:24:48Z)
• The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey [0.0]
  This paper examines the recent advancements in AI agent implementations. It focuses on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities.
  arXiv  Detail & Related papers  (2024-04-17T17:32:41Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• A Reference Architecture for Designing Foundation Model based Systems [28.826700360670515]
  There is a broad consensus that foundations models will be the fundamental building blocks for future AI systems. incorporating foundations models into AI systems raises significant concerns about responsible and safe AI. The paper proposes a pattern-oriented reference architecture for designing responsible foundation-model-based systems.
  arXiv  Detail & Related papers  (2023-04-13T05:01:03Z)
• Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
  This paper considers an evolutionary approach to creating a cognitive functionality. We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
  arXiv  Detail & Related papers  (2022-07-02T12:41:32Z)
• A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning [104.3643447579578]
  We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state. The design allows agents to learn to plan effectively, by attending to the relevant objects, leading to better out-of-distribution generalization.
  arXiv  Detail & Related papers  (2021-06-03T19:35:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.