Today’s multi-agent systems deploy teams of AIs that can do much more than previous generations of machines. Along with running operations for organizations, these systems also help scale them up and improve their decision-making. In addition, autonomous AI is on the verge of enabling a whole new level of independent automation.
Companies that embrace these groundbreaking technologies enjoy a whole new level of efficiency. As a result, they can shove their competition aside. These agents rely heavily on the capabilities and tools of the large language models (LLMs) on which they have been built, which means that multi-agent systems will continue improving as the technology matures.
A brief history of AI
Most people can remember a frustrating conversation they have had with an early customer-service chatbot. People usually call in specifically because they have a rare situation that requires personalized troubleshooting. In many cases, however, the robot was limited to the information the person had already found on the organization’s website or by visiting their individual account.
This made the whole conversation a maddening waste of time, and the automated system became a maze that the customer needed to solve. The prize for success? An actual human being on the other end capable of action or decision.
As this example illustrates, the first widely used commercial chatbots might have been cost-effective and easy for organizations to set up, but they were narrow-minded and rule-bound. They weren’t flexible, couldn’t reason or adapt, and they certainly had no ability to strategize. There were also limits on how much they could be scaled up.
That started to change in November 2022, when LLMs and Generative AI erupted onto the world stage. These sophisticated models comprehended context with greater adaptability and fluency. As a result, they could interact with people much better and even give the impression of creativity. Even demanding users found that these systems could meet their needs. The level of sophistication turned them into agents reliable enough to delegate decisions and trust them with taking action.
Indeed, according to a 2023 study on generative AI from user experience research and design company Nielsen Norman Group, customer service representatives successfully served 13.8 percent more customers per hour with help from generative AI. Programmers produced 126 percent more code each week with its assistance. Other professionals boosted the amount they could write by 59 percent per hour.
Unfortunately, sometimes these AIs also serve up nonsense — AI is now famous for its hallucinations. Researchers have improved their accuracy through several methods, such as Retrieval Augmented Generation (RAG), fine-tuning, reinforcement, and statistical tools. Of course, there’s still no substitute for expert human oversight.
In addition, concerns about bias persist. That’s why using Explainable AIs (XAI) that can describe how they arrived at their decisions is vital for establishing credibility or pointing toward room for improvement.
Meanwhile, AI has leveled up once again with the arrival of multi-agent systems and autonomous AI.
Multi-agent systems, explained
If one somewhat powerful AI is getting good enough, a whole team of them is better. That’s the basic idea behind multi-agent systems.
Multi-agent systems instantiate a group of AI models and give each member different tasks that can be seen as narrow functions of a job or the itemized expectations you would break down in a job description. Every agent focuses the full force of its power on its own unique domain, just like the workforce at a company — different people apply themselves to filling their particular jobs.
For example, multi-agent systems are frequently used in software development. While one AI writes code, another ensures the language aligns with the required criteria. Yet another acts as the cybersecurity expert, probing the code for vulnerabilities. A separate intelligence handles quality control.
Also, just like the employees at a company, who need to consult with each other occasionally, these agents coordinate amongst themselves. They ask questions and share information. At times, they can even make collective decisions.
Autonomous AI may also be added to the mix. Unlike the agents, their main goal isn’t to complete particular activities but to strive for the system’s independence. While human oversight is still a good idea, these systems can form a dedicated team that can largely be trusted to carry on unsupervised, which has important implications for the design of modern tech stacks.
The benefits of multi-agent systems
Organizations have begun tethering multiple agents and autonomous AIs together because they started bumping up against the limits of what one single agent could do. Just like a single human CEO couldn’t possibly do all of the jobs in their company and be able to scale up, generative AIs — even extremely powerful ones — have a limit. Start investing in more AIs and putting them in groups, however, and watch your capacity expand.
Indeed, these systems can handle much more complexity than their predecessors. One example comes from the realm of cybersecurity, where these networks of autonomous machines go far beyond sensing possible breaches and reporting them to human staff. Today’s autonomous AIs can determine whether or not a given situation should be escalated and even recommend possible solutions. Moreover, they can do this much faster than human staff would be able to.
This capacity for contextual understanding means businesses can automate more workflows than previously, including those requiring judgment, prioritization, and problem-solving. Since workers don’t get tired, hungry, or thirsty, this work can also usually proceed without interruption. And since AI has lightning-fast reflexes, it can respond to changes on the ground even faster than a human being could.
At any given time, the organization’s leadership can also add a new agent or autonomous AI, allowing them to scale their operations up without a complete IT overhaul.
AI enables organizations to seize competitive advantages
Given advantages like these, it’s no surprise that autonomous AI and multi-agent systems are revolutionizing tech stacks. Automation will never be the same. Yesterday’s chatbots and today’s networks reside in different universes.
However, as impressive as today’s autonomous AI and multi-agent systems are, they are still just the beginning. Over the coming year, I anticipate AI will improve its ability to understand different types of data, not just text and images, but audio and video. They could also learn how to use real-time data better and refine their internal communications.
At some point — perhaps in the next three years — I predict AIs will start redesigning their own workflows in brilliant ways that surprise us with their efficiency and cost effectiveness. Someday, we could even see fully automated manufacturing plants or supply chains. Multi-agent systems could combine, creating whole ecosystems of interconnectivity and could even manage an economy of their own to remain cost-effective and constantly operate with minimal resources.
AI is on the cusp of redefining not just whole industries, but also the future of global innovation. It’s up to us to shape this revolution so nobody gets left behind.
