Agent Guidance Language (AGL) is a natural language structured writing method inspired by Standard Operating Procedures (SOP), enabling business experts to write "executable instruction manuals" for Agents in natural language, injecting experts' preferences, private domain knowledge, and tacit rules into the task execution process. Through templating, AGL enables business experts to describe tasks clearly and completely, considers Agent adaptability, and greatly improves the stability of Agent task execution.
What is AGL
AGL (Agent Guidance Language) is a natural language structured writing method inspired by Standard Operating Procedures (SOP), enabling business experts to write "executable instruction manuals" for Agents in natural language, injecting experts' preferences, private domain knowledge, and tacit rules into the task execution process.
How AGL works
- Structured task description:AGL draws on the concept of Standard Operating Procedures (SOP) from management science, breaking tasks down into three core parts:
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Problem overview: Clarify the task's goals and background.
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Required resources: List all resources and tools needed to complete the task.
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Step descriptions: Describe in detail the specific steps to complete the task.
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- This structured approach ensures the clarity and completeness of task descriptions while also making it easier for Agents to understand and execute them.
- Natural language expression:AGL is written in natural language, allowing business experts to describe tasks as if writing a process manual. It is easy to understand and write, and enables Agents to better parse and execute tasks through natural language processing (NLP) technology.
- Improving Agent adaptability: AGL fully considers Agent adaptability in its design, ensuring that task guidance manuals can be understood and executed by Agents. Through standardized natural language expression, AGL can inject experts' preferences, private domain knowledge, and tacit rules into the task execution process, improving Agent performance in specific domains.
- Templating and community co-building: AGL provides a standard template, and business experts can fill in task content according to the template to generate standardized guidance manuals. In addition, the AGL project is co-built by the community in open-source form on GitHub, and users can place their own guidance manuals in the
/examples/directory and initiate a PR for the community to jointly refine and improve. - Self-check and quality assurance: AGL provides suggestions on how to self-check the quality and stability of guidance manuals. Through these verification mechanisms, business experts can ensure the quality of guidance manuals and improve the stability of Agent task execution.
Main applications of AGL
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Enterprise automation task guidance: AGL can write detailed execution manuals for various enterprise automation tasks, helping Agents stably execute tasks according to established processes, reducing execution deviations caused by vague or incomplete task descriptions, and improving enterprise operational efficiency.
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Customer service and customer support: In the customer service field, AGL can be used to write standard customer issue handling processes, transforming experts' experience and handling rules into executable instructions for Agents, enabling Agents to solve customer issues more accurately and improve customer satisfaction.
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Data analysis and processing: AGL can write data processing and analysis guidance manuals for data analysis Agents, clarifying data cleaning, analysis steps, and result presentation methods, ensuring the accuracy and consistency of data analysis and assisting enterprise decision-making.
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Intelligent Robot Task Planning: AGL can be used to write task planning manuals for intelligent robots, guiding robots to complete complex operational tasks, such as cargo handling in logistics warehousing and parts assembly on industrial production lines, improving the stability and accuracy of robot task execution.
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Content Creation and Review: In the field of content creation, AGL can write creation guidelines for content creation Agents, standardizing content style, structure, and review standards, helping Agents generate high-quality content that meets enterprise requirements while improving the efficiency and accuracy of content review.
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Education and Training: AGL can be used to write teaching guidance manuals, providing educational Agents with teaching content, teaching methods, and evaluation standards, assisting educational Agents in better completing teaching tasks and improving teaching effectiveness and student learning experience.
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Medical and Health Assistance: In the medical field, AGL can write guidance manuals for medical Agents on case analysis, diagnostic recommendations, and treatment plan formulation, transforming experts' medical knowledge and experience into executable instructions for Agents, assisting medical personnel in diagnosis and treatment, and improving the quality and efficiency of medical services.
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Financial Risk Assessment and Management: AGL can be used to write guidance manuals for financial risk assessment and management, providing financial Agents with risk identification, assessment, and response strategies, helping Agents more accurately assess financial risks, optimize risk management processes, and safeguard the stable operation of financial institutions.
Challenges Facing AGL
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The Complexity of Natural Language Understanding: Although AGL is written in natural language, the ambiguity and diversity of natural language may make it difficult for Agents to accurately understand certain expressions in task guidance manuals, especially when industry terminology or tacit knowledge is involved.
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The Difficulty of Standardizing Task Descriptions: Different business experts have different expression styles and habits, which may lead to task descriptions that are not sufficiently standardized or that omit key information, affecting the Agent's understanding and execution of tasks.
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Agent Adaptability Issues: Although AGL takes Agent adaptability into consideration, different Agents have different capabilities and limitations. Some task guidance manuals may need customized adjustments for specific Agents, increasing the complexity of implementation.
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Coordination Challenges of Community Co-construction: The AGL project relies on community co-construction. Differences in the level and understanding of different contributors may lead to uneven quality of guidance manuals, requiring effective mechanisms to ensure the quality and consistency of community contributions.
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Insufficient Adaptability to Dynamic Tasks: In rapidly changing business environments, task requirements may change frequently. AGL guidance manuals need to be updated in a timely manner to adapt to new task requirements; otherwise, Agents may execute outdated tasks.
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Generalizability Issues in Cross-domain Applications: AGL needs to be applied in different domains, and the professional knowledge and rules of each domain vary greatly. How to design a guidance language that is both generalizable and able to meet the needs of specific domains is a challenge.
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Performance and Efficiency Issues: In practical applications, the parsing and execution of AGL guidance manuals may affect the performance and efficiency of Agents, especially when handling complex tasks. Optimization is needed to ensure that Agents can respond quickly.
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Security and privacy issues: The AGL guidance manual may contain sensitive information or private data. How to ensure the security and privacy of this information and prevent data leakage or malicious use is an important issue that needs to be addressed.
AGL project address
- GitHub repository：https://github.com/dataelement/AgentGuidanceLanguage
AGL's development prospects
As an emerging structured natural language authoring method, Agent Guidance Language (AGL) has broad development prospects. With the rapid development of artificial intelligence and automation technology, enterprises are increasingly dependent on Agents. AGL can effectively solve the stability and adaptability issues in Agent task execution, helping business experts transform professional knowledge and tacit rules into guidance manuals that Agents can execute. AGL's open-source model and community co-building mechanism can continuously optimize and improve, adapting to the needs of different fields and scenarios. AGL is expected to be widely applied in many fields such as enterprise automation, intelligent customer service, data analysis, and intelligent robots, becoming an important tool for improving Agent performance and efficiency.