Intelligent document generation: Tailoring a multi-agent system to your use case
The ability to quickly generate high-quality documents is incredibly valuable. Whether for business reports, academic papers or technical manuals, ensuring that documents are cohesive, accurate and tailored to your audience can be a challenging task.
By leveraging the power of Multi-Agent Systems (MAS) combined with Generative AI, organizations can create a robust solution configured to streamline the document writing process. A system like this can not only automate the document creation process but also allow for real-time adjustments based on user feedback, ensuring that each document meets standards of quality and relevance.
What are multi-agent systems (MAS)?
For the purposes of this article, we won’t go too deep into the evolution of MAS and how they work. My Atos colleague Purshottam Purswani already wrote an excellent article on this topic. If you are unfamiliar with the idea of multi-agent systems, take a few minutes to read that first.
Put simply, multi-agent systems consist of multiple AI agents, each with specific roles and responsibilities. The concept emerged in the 1980s from distributed AI research. The intent is to decentralize intelligence across independent agents that collaborate to solve complex problems.
Inspired by social behaviors in nature, MAS have evolved significantly along with advancements in computing power and algorithms. Today, they are used in fields such as robotics, logistics, finance and healthcare.
MAS and generative AI: A powerful combination
Combining MAS with generative AI — particularly large language models (LLMs) like GPT-4o — creates powerful synergies. While LLMs excel in generating text rapidly, they often struggle when given anything more than a simple problem to solve.
A multi-agent system breaks problems down into manageable sub-tasks, each handled by specialized agents. These agents focus on their specific tasks and combine their outputs to produce a cohesive final result. Transparency is essential, because clear insights into the communication between agents and their decision-making processes helps build trust in the system.
Multi-agent systems have evolved significantly along with advancements in computing power and algorithms. Today, they are used in fields such as robotics, logistics, finance and healthcare.
Streamlining document creation through MAS
So how can this be applied to the task of creating documentation? We know that writing documents can be a time-consuming process. You must ensure they are concise, accurate, complete, written in an appropriate tone for your audience, and you should maintain consistency and uniformity in style and word choice.
With these challenges in mind, a multi-agent system can be designed to assist you throughout the document creation process. By simply providing your input data, the system will generate a document within minutes, making the task more manageable and efficient. Let’s dive deeper.
Tailoring MAS to your needs
To ensure the system meets your needs, it must undergo a one-time setup that integrates your business logic. This setup involves creating expert agents specialized in various aspects of the document generation, such as tone, conciseness, word choice and consistency. Each agent is responsible for its respective task to ensure the quality of the generated document. The setup involves repeatedly evaluating outputs and fine-tuning the agents to align with your preferences.
During this initialization, you can also specify which chapters and subheadings should be included in the generated document. However, the system can dynamically adapt its layout in real-time based on the input information provided by the user. For instance, if certain information is missing, the system can automatically choose to omit or add relevant chapters. This flexibility allows a single system to generate documents of varying lengths and structures.
Adding interactivity
Regardless of the use case, the most intelligent and crucial agent in the system is always you, the end user. Human interaction is a key feature of any multi-agent system. You can engage as a human-in-the-loop at various stages throughout the system's operation, most notably:
Input data verification
The quality of the data you feed into the system is crucial, as it influences the quality of the generated document. Any errors or missing information will impact the final result. To address this, the system can be configured to set standards for your input data. If the data provided does not meet these standards, the system will prompt the user to fill in any missing information or correct any errors. Once the system verifies that the input data meets the required standards, the document generation process can begin.
User feedback and revisions
If you are not satisfied with one or more chapters that have been generated, you can provide additional feedback. The system will consider your feedback to rewrite those chapters and may adjust other chapters for consistency. You can also choose whether your remarks should take priority over the standards defined during the system's initialization.
What’s next for MAS?
The following topics could be interesting extensions to a multi-agent system. Due to the adaptable and modular nature of MAS, adding new capabilities is relatively straightforward. You just need to create and configure a new agent to handle the functionality you want to add. They might include:
Integrating visuals with text
Currently, the system only generates text output, but integrating visuals could be an exciting next step. Instead of using an image generation model like DALL-E, specialized agents could be developed to generate highly relevant plots or diagrams based on the input data.
Math agents for complex operations
To save even more time, you could enable the system to perform calculations on your data as it generates the document. Instead of doing the math yourself, simply feed the raw data into the system and the results could be integrated into the document. Implementing such a feature would require extra focus on transparency and accuracy for these agents, because the results must be verifiable.
There are just a few of the ways that we’re exploring to extend the capabilities of MAS. I would be interested to hear your ideas about what new capabilities you would like to see integrated into MAS, so don’t hesitate to share your thoughts with me.
Posted on: January 28, 2025
Elias De Deken
Artificial Intelligence ConsultantMember, Atos Research Community
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