SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs (2501.09316v1)

Abstract: Despite significant advancements in general-purpose AI agents, several challenges still hinder their practical application in real-world scenarios. First, the limited planning capabilities of LLMs (LLM) restrict AI agents from effectively solving complex tasks that require long-horizon planning. Second, general-purpose AI agents struggle to efficiently utilize domain-specific knowledge and human expertise. In this paper, we introduce the Standard Operational Procedure-guided Agent (SOP-agent), a novel framework for constructing domain-specific agents through pseudocode-style Standard Operational Procedures (SOPs) written in natural language. Formally, we represent a SOP as a decision graph, which is traversed to guide the agent in completing tasks specified by the SOP. We conduct extensive experiments across tasks in multiple domains, including decision-making, search and reasoning, code generation, data cleaning, and grounded customer service. The SOP-agent demonstrates excellent versatility, achieving performance superior to general-purpose agent frameworks and comparable to domain-specific agent systems. Additionally, we introduce the Grounded Customer Service Benchmark, the first benchmark designed to evaluate the grounded decision-making capabilities of AI agents in customer service scenarios based on SOPs.

Overview of the SOP-Agent Framework for Domain-Specific AI Applications

This paper introduces the Standard Operational Procedure-guided Agent (SOP-agent), a framework designed to construct domain-specific AI agents by integrating pseudocode-style Standard Operational Procedures (SOPs) written in natural language. SOP-agent addresses key limitations inherent in general-purpose AI agents, particularly their inadequate planning capabilities and inefficiency in utilizing domain-specific knowledge.

Core Proposition

The SOP-agent proposes a systematic approach to integrating domain-specific workflows into AI operations. SOPs are represented as decision graphs, allowing AI agents to navigate tasks effectively. This method offers a structured means to incorporate detailed domain knowledge into AI decision-making, surpassing the generic abilities of LLMs alone. Key to this framework is its capability to provide a filtered set of tools and actions aligned with the SOP, enhancing decision-making precision and efficacy.

Experimental Validation

Extensive experiments were conducted across varied domains such as decision-making, question-answering, code generation, and data cleaning. These experimental validations underscore SOP-agent's superiority and versatility over existing systems like AutoGPT and ReAct. Noteworthy results include:

• A 66.2% improvement over AutoGPT in zero-shot settings on the ALFWorld benchmark.
• Competitive performances on standard benchmarks such as HumanEval with a Pass@1 score of 86.6 and the MBPP benchmark scoring 89.5.
• A 100% success rate in data cleaning tasks, outperforming AutoGPT and aligning closely with domain-specific systems.

Implications and Future Work

The SOP-agent framework highlights the potential for AI systems to transition from general-purpose use cases to more specialized and accurate domain-specific applications. By leveraging structured workflows that mimic human expertise and processes, SOP-agents can handle complex real-world tasks with increased reliability and specificity. This approach paves the way for AI systems that are not only robust but also more adaptable to continuous changes in domain-specific procedures.

The research opens avenues for future developments in SOP engineering—a process of refining and optimizing SOPs to further increase the robustness and accuracy of AI agents. This involves iteratively enhancing SOPs based on empirical findings, ensuring logical coherence, and aligning with operational needs. Moreover, the introduction of a Grounded Customer Service Benchmark demonstrates the framework's applicability in evaluating AI systems in grounded decision-making contexts, emphasizing its relevance in commercial customer service applications.

Overall, the SOP-agent marks a significant step towards embedding domain-specific knowledge into AI systems, facilitating their application in specialized fields and imbuing them with the versatility required to adapt to evolving procedural contexts. Future explorations could further expand on integrating real-time data inputs and refining SOPs for broader industrial applications, potentially transforming many sectors reliant on precise and adaptive AI assistance.