Following the rise of ChatGPT, generative AI (gen AI) has been at the center of attention for businesses and engineers around the world. Unlike traditional AI, these new large language models (LLMs) can process language similar to how a human would – and they’re rapidly re-shaping the landscape of automated technologies.

If you need assistance with generating content, answering questions from customers, or summarizing long documents, generative AI can ease the load and streamline the execution of those tasks.

As magical as these large language models may seem, they aren’t a catch-all solution for any task requiring human-like processing. For more involved problems that require complex or multi-step reasoning, LLMs fall short in processing and arriving at a sufficient solution. The knowledge available to LLMs is limited to the information from their training data, making them out-of-date with the most recent data and news. LLMs can produce made-up data as well, called hallucinations, without any way to verify or cross-check information. Additionally, LLMs can only handle tasks one by one and therefore are unequipped to break down and handle intricate problems. Even more critical, the sequential approach of LLMs slows response times, leaving humans and systems impatiently waiting.

There is one existing solution that can mitigate some of these limitations, called RAG (Retrieval Augmented Generation). RAG enhances the capabilities of LLMs by providing access to data beyond the training data. However, RAG can only retrieve information from pre-determined sources of knowledge. AI agents bridge the gap between LLMs’ potential and limitations to provide a solution for processing and handling complex tasks.

AI agents can solve problems that LLMs and RAG alone are unequipped to handle.

An AI agent is an intelligent system that can make decisions and execute tasks autonomously. They’re built around LLMs, giving them natural language skills, and they can also interact with their environment via tools, expanding the possibilities of actions they can complete. An AI agent can conduct its own internet searches, collect and analyze data, and create diverse content.

You can think of an agent as an expert who excels at performing tasks related to their skillset. If you assemble a team of agents, they can tackle even more intricate and demanding problems. A team of AI agents can delegate tasks, utilize their special skills, and learn from each other to achieve their goal in a smarter, more efficient manner. Just like humans, AI agents can accomplish difficult problems better as a team than as individuals. This idea is the foundation for multi-agent systems and their added value.

Key Advantages of Multi-Agent Systems in Gen AI

The benefits of multi-agent systems go beyond performing basic tasks. Here are some of the top reasons to use them in your gen AI strategy.

Enhanced Functionality with Tools

Multi-agent systems take generative AI to the next level by not only processing instructions but also executing actions using a variety of tools.

Tools are add-ons that allow agents to interact with other systems and complete tasks such as web searching, interpreting code, and conducting data analysis. The ability to integrate real-time data and computational power enables AI agents to overcome the limitations of LLMs and to become more reliable. By leveraging tools, multi-agent systems can make more informed decisions and optimize performance.

As an example, market research can be automated using a multi-agent system built with web search and data analysis tools. The agents can gather real-time data from social media, competitors, and industry reports. Together, they can reduce the need for manual research and make informed decisions based on up-to-date market insights and trends.

Teamwork in Action

The true potential of multi-agent systems lies in their ability to share information and coordinate tasks to achieve their common goal. Like a team of experts, AI agents can pass off tasks to others who are better suited, ensuring that each task is handled by the best agent. For tasks that aren’t reliant on each other, agents can work in parallel and distribute the processing load across agents in the system.

An advantage of dividing tasks is that the overall functionality can be distributed across the systems, as agents simultaneously handle multiple tasks and queries. With a single agent, productivity is inherently limited by the context window of their LLM, which is the amount of information a LLM can retain at once. With multi-agent systems, information can be split up across context windows, and agents can maintain an understanding of the overall workflow through inter-agent communication.

A sales team can leverage multiple specialized AI agents to handle lead generation in a sales pipeline. For example, one agent can collect from different sources to identify potential leads. Another agent can analyze the leads to find the most qualified ones. And a third agent can compile these into a list enriched with contact information and data about past interactions.

Smart and Adaptable

These systems are highly flexible and allow for easy reconfiguration, whether it’s adding new agents or modifying existing ones to meet evolving business needs. Agents can be equipped with different LLMs tailored to specific functions, and they can be specialized with distinct backgrounds, goals, and perspectives.

This versatility enables the system to handle a wide range of use cases effectively. Beyond manual reconfiguration, some agents can also learn from their errors adjust their workflow to address tasks that aren’t completed well. By sharing knowledge and providing feedback, agents can make intelligent decisions to modify and to optimize their course of action.

A multi-agent system can dynamically adjust financial reporting and forecasting to meet evolving business needs. For instance, new agents can be quickly created and integrated to monitor market-specific financial data when a company enters a new market. The existing agents can easily recalibrate their analysis to consider this new data to ensure that the company receives accurate financial insights to make strategic decisions as conditions change.

Agents Are the Next Step in Your Gen AI Strategy

Multi-agent systems offer a powerful way to autonomously optimize business operations, expanding upon the power of large language models and RAG. With their ability to specialize, collaborate, and adapt, these agentic systems can boost efficiency and productivity for a variety of enterprise use cases.

While multi-agent systems bring many benefits, they aren't without limitations. They still require human oversight and may encounter errors in breaking down and accomplishing tasks. One advantage is that they offer greater transparency compared to LLMs, making it easier to monitor and resolve issues.

As generative AI continues to advance, multi-agent systems will evolve and be able to solve larger and even more sophisticated problems. Over the past year, three major open-source frameworks have emerged – CrewAI, LangGraph, and Microsoft’s Autogen – and they are already shifting the landscape of automation.

RapidScale’s team of AI experts is here to help you launch your AI strategy. If you’re ready to get started, contact our team today.