Refining enterprise knowledge with multi-agent GenAI models
In a previous blog post about AI-powered incident response, I discussed the promising role of generative AI (GenAI) in streamlining incident remediation processes. However, when delving deeper into the implementation, a crucial challenge became apparent – the quality of the enterprise knowledge base articles themselves.
Picture this: As an IT manager, you constantly struggle to keep your team updated on the latest technical knowledge and troubleshooting procedures. With the rapid pace of technological change threatening to render your technical documentation out-of-date, how can you rely on GenAI-based assistance?
GenAI excels at providing concise and structured recommendations, but its effectiveness is directly tied to the quality and completeness of the information it draws from. If the underlying knowledge base is outdated, incomplete or inconsistent, the output from GenAI will inevitably have the same shortcomings.
In short, any errors or gaps in your documentation will be reflected in the advice that GenAI provides. Clearly, the key to leveraging AI for remediation guidance is keeping your knowledge base complete and current, but how?
What is a multi-agent system?
Simply put, a multi-agent system (or multi-agent framework) like Semantic Kernel, AutoGen or Crew.AI is a group of intelligent autonomous decision-making AI agents that interact with each other to achieve a common goal. For example, the agents may retrieve information, question each other or verify each other’s outputs, but none of them is “in command,” so to speak. Each has an independent role to play, and only by working together can they find the best solution.
A collaborative approach to improving enterprise knowledge with multi-agent frameworks
I've been closely following the exciting advancements in the generative AI field. During this exploration, I came across the concept of multi-agent systems and decided to investigate its potential to generate knowledge articles.
That's where the multi-agent framework comes in. The multi-agent model lets us develop applications involving multiple autonomous agents collaborating to achieve complex goals. Here, the goal is to generate incident remediation knowledge articles and the agents will orchestrate and generate the content as follows:
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- Information gathering agents continuously search product documentation, online forums and industry resources for the latest technical information, troubleshooting procedures and best practices.
- Information retrieval agents integrate the information gathered into your existing knowledge base, providing a starting point for generating new knowledge base articles or updating the existing ones.
- Vision agents are specialized AIs that can analyze diagrams, screenshots and other visual content to add helpful descriptions, annotations and commentary to supplement the textual information.
- Knowledge synthesis agents use GenAI models to process and synthesize all the information gathered, turning it into clear, structured knowledge base articles covering a wide range of topics.
- Quality assurance agents play a crucial role, reviewing each article for against criteria such as accuracy, completeness, readability, and adherence to organizational standards. If any article doesn’t meet the standards, the QA agents initiate a rewrite using generative AI.
- Version control agents manage updates to the knowledge base, tracking changes and providing an auditable history integrated with your existing systems.
Most importantly, the last step in the process is a human in the loop who reviews each knowledge article generated, providing a final check for accuracy and verifying that the content is not an AI hallucination.
Using this approach, I decided to use AutoGen to pilot a multi-agent framework that generates knowledge base articles. It generated more than 20 articles and I found the quality to be high enough to feed into my incident remediation process. My team gained access to a self-sustaining knowledge base powered by generative AI, enabling them to handle incidents more effectively and tackle complex issues with confidence.
The generative AI advantage
Integrating GenAI models for knowledge synthesis and rewriting has the potential to establish a self-sustaining ecosystem that continuously improves the quality and relevance of your enterprise knowledge base. This model has several significant benefits:
- Comprehensive Coverage: GenAI can draw upon vast amounts of technical data, eliminating tedious manual cataloging and retrieval work, and enabling the generation of knowledge base articles that cover a wide range of topics and scenarios.
- Natural Language Generation: GenAI excels at producing human-readable content, ensuring that knowledge base articles are clear, concise, and easy to understand for engineers of varying experience levels.
- Continuous Learning: By incorporating feedback loops and machine learning techniques, GenAI models can continuously improve, adapting to new information and constantly refining the quality of the content it generates.
The road ahead
Implementing this integrated approach involving GenAI and multi-agent systems has its challenges, such as data quality, model training, agent coordination and performance optimization. However, the potential benefits of a self-sustaining, high-quality knowledge base are substantial, helping enterprises adopt Generative AI initiatives for operational excellence.
While my initial explorations have been for IT knowledge management and incident remediation, I believe this AI-driven approach has immense potential for broader enterprise-wide applications across many domains and industries. The multi-agent AutoGen framework can be adapted to generate complete, coherent knowledge bases for diverse fields such as finance, healthcare, manufacturing and legal, among others.
Posted on: April 30, 2024
Purshottam Purswani
Chief Technology Officer, Atos Asia PacificMember, Atos Research Community
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