Action Engine: Automatic Workflow Generation in FaaS

• URL: http://arxiv.org/abs/2411.19485v2
• Date: Wed, 20 Aug 2025 16:32:06 GMT
• Title: Action Engine: Automatic Workflow Generation in FaaS
• Authors: Akiharu Esashi, Pawissanutt Lertpongrujikorn, Shinji Kato, Mohsen Amini Salehi,
• Abstract summary: Action Engine makes use of toolaugmented large language models (LLMs) at its kernel to interpret human language queries.<n>Action Engine seamlessly manages the data dependency between them, ensuring the developer's query is processed and resolved.
• Score: 1.4185188982404757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Function as a Service (FaaS) is poised to become the foundation of the next generation of cloud systems due to its inherent advantages in scalability, cost-efficiency, and ease of use. However, challenges such as the need for specialized knowledge, platform dependence, and difficulty in scalability in building functional workflows persist for cloud-native application developers. To overcome these challenges and mitigate the burden of developing FaaS-based applications, in this paper, we propose a mechanism called Action Engine, that makes use of tool-augmented large language models (LLMs) at its kernel to interpret human language queries and automates FaaS workflow generation, thereby, reducing the need for specialized expertise and manual design. Action Engine includes modules to identify relevant functions from the FaaS repository and seamlessly manage the data dependency between them, ensuring the developer's query is processed and resolved. Beyond that, Action Engine can execute the generated workflow by injecting the user-provided arguments. On another front, this work addresses a gap in tool-augmented LLM research via adopting an Automatic FaaS Workflow Generation perspective to systematically evaluate methodologies across four fundamental sub-processes. Through benchmarking various parameters, this research provides critical insights into streamlining workflow automation for real-world applications, specifically in the FaaS continuum. Our evaluations demonstrate that the Action Engine achieves comparable performance to the few-shot learning approach while maintaining platform- and language-agnosticism, thereby, mitigating provider-specific dependencies in workflow generation. We notice that Action Engine can unlock FaaS workflow generation for non-cloud-savvy developers and expedite the development cycles of cloud-native applications.

Related papers

• MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks [95.86122998005612]
  MiroFlow is an open-source agent framework for large language models (LLMs)<n>It incorporates an agent graph for flexible orchestration, an optional deep reasoning mode to enhance performance, and a robust execution to ensure stable and reproducible performance.<n>It consistently achieves state-of-the-art performance across multiple agent benchmarks, including GAIA, BrowseComp-EN/ZH, HLE, xBench-DeepSearch, and FutureX.
  arXiv  Detail & Related papers  (2026-02-26T09:45:04Z)
• WISE-Flow: Workflow-Induced Structured Experience for Self-Evolving Conversational Service Agents [12.014029662322152]
  Large language model (LLM)-based agents are widely deployed in user-facing services but remain error-prone in new tasks.<n>We propose WISE-Flow, a feasibility-centric framework that converts historical service interactions into reusable procedural experience.
  arXiv  Detail & Related papers  (2026-01-13T02:43:41Z)
• Learning API Functionality from Demonstrations for Tool-based Agents [1.3332982107151432]
  We propose learning API functionality directly from demonstrations as a new paradigm applicable in scenarios without documentation.<n>We study how the number of demonstrations and the use of LLM-generated summaries and evaluations affect the task success rate of the API-based agent.<n>We find that providing explicit function calls and natural language critiques significantly improves the agent's task success rate due to more accurate parameter filling.
  arXiv  Detail & Related papers  (2025-05-30T04:17:09Z)
• Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs [63.10710876536337]
  We propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts. Our framework comprises two components: (1) task creation, using top-down functionality and bottom-up API synergy exploration to generate helpful tasks. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs.
  arXiv  Detail & Related papers  (2025-04-29T04:03:37Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use. MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools. Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• Flow: Modularized Agentic Workflow Automation [53.073598156915615]
  Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of agentic during execution has not been well studied. In this paper, we define an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by agents. Our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance.
  arXiv  Detail & Related papers  (2025-01-14T04:35:37Z)
• Histrio: a Serverless Actor System [44.99833362998488]
  Histrio is a programming model and execution environment that simplifies the development of stateful applications. It lifts concerns such as state management, database interaction, and programming handling from developers. It guarantees exactly-once-processing consistency, meaning that the application always behaves as if any interaction with external clients was processed once and only once.
  arXiv  Detail & Related papers  (2024-10-29T06:58:56Z)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorFBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures. We also present WorFEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms. We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• Unleashing the Power of Task-Specific Directions in Parameter Efficient Fine-tuning [65.31677646659895]
  This paper focuses on the concept of task-specific directions (TSDs)-critical for transitioning large models from pretrained states to task-specific enhancements in PEFT. We introduce a novel approach, LoRA-Dash, which aims to maximize the impact of TSDs during the fine-tuning process, thereby enhancing model performance on targeted tasks.
  arXiv  Detail & Related papers  (2024-09-02T08:10:51Z)
• Object as a Service: Simplifying Cloud-Native Development through Serverless Object Abstraction [1.7416288134936873]
  We propose a new paradigm, known as Object as a Service (O) that encapsulates application data and functions into the cloud object abstraction. O relieves developers from resource and data management burden while offering built-in optimization features. We develop a platform named Oparaca that offers state abstraction for structured and unstructured data with consistency and fault-tolerant guarantees.
  arXiv  Detail & Related papers  (2024-08-09T06:55:00Z)
• AutoFlow: Automated Workflow Generation for Large Language Model Agents [39.72700864347576]
  Large Language Models (LLMs) have shown significant progress in understanding complex natural language. To make sure LLM Agents follow an effective and reliable procedure to solve the given task, manually designed are usually used. We propose AutoFlow, a framework designed to automatically generate for agents to solve complex tasks.
  arXiv  Detail & Related papers  (2024-07-01T21:05:02Z)
• FlowMind: Automatic Workflow Generation with LLMs [12.848562107014093]
  This paper introduces a novel approach, FlowMind, leveraging the capabilities of Large Language Models (LLMs) We propose a generic prompt recipe for a lecture that helps ground LLM reasoning with reliable Application Programming Interfaces (APIs) We also introduce NCEN-QA, a new dataset in finance for benchmarking question-answering tasks from N-CEN reports on funds.
  arXiv  Detail & Related papers  (2024-03-17T00:36:37Z)
• Couler: Unified Machine Learning Workflow Optimization in Cloud [6.769259207650922]
  Couler is a system designed for unified ML workflow optimization in the cloud. We integrate Large Language Models (LLMs) into workflow generation, and provide a unified programming interface for various workflow engines. Couer has successfully improved the CPU/Memory utilization by more than 15% and the workflow completion rate by around 17%.
  arXiv  Detail & Related papers  (2024-03-12T12:47:32Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• Object as a Service (OaaS): Enabling Object Abstraction in Serverless Clouds [2.0575037267955305]
  We propose a new abstraction level atop the function abstraction, known as Object as a Service (O) programming. O encapsulates the application data and function into the object abstraction and relieves the developers from resource and data management burdens. It also unlocks opportunities for built-in optimization features, such as software reusability, data locality, and caching.
  arXiv  Detail & Related papers  (2022-06-10T21:31:22Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)

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.