Stepping into the future: unlocking the power of agentic automation and multi-agent systems

Chatbots are old news, welcome to the era of AI agents. These intelligent systems don’t just respond to commands but think, plan, and act independently. They’re able to understand your needs, provide solutions, and make your life easier. But what happens when these agents can interact with each other? We give birth to agentic AI architectures: multi-agent systems. 

Multi-Agent Systems (MAS) think, decide, and act by working collaboratively with each other, which brings a greater level of “autonomy”. In the context of enterprise workflows, this represents a shift from “AI workflow automation” based on predefined conditions powered by code, to “agentic automation” based on real-time predictions, powered by models.  

It’s important to realise that both types of automation are valuable, but agentic automation introduces a new way of solving “undefinable” tasks, scaling workflows and creating impact.Read on to find out how multi-agents are poised to shape the future of enterprise AI. 

What are multi-agent systems (MAS)? 

Unlike single AI agents, multi-agent systems consist of a team of AI agents collaborating with each other to solve complex problems, manage large-scale tasks, and deliver faster, more dynamic solutions. These systems represent a shift from workflow automation, where all sequences and steps are scripted, to agentic automation, where agents perceive their environment and adapt according to it. 

Why this matters for enterprises... 

According to Dimension Market Research, the multi-agent system market is projected to grow from $6.3 billion in 2025 to $184.8 billion by 2034. This dramatic leap highlights an undeniable change in how organisations are likely to experience AI agents in the coming years. Here's how MAS can help enterprises, and why they are likely to want to jump on this innovation wave: 

1. Managing large workloads faster and more effectively 

Organisations can unlock over 60% potential efficiency gains and more than $3 million savings annually when leveraging multi-agent systems, according to a Mckinsey survey. This is because multi-agent systems excel at handling extensive, distributed workloads. Unlike single agents, where processing power and resources are concentrated in a single entity, these smart systems collaborate, enabling parallel processing and faster completion of tasks.   

2. Handling complex tasks with dedicated expertise 

We can reduce task completion times by up to 86% when multi-agent systems handle time-consuming processes like data analysis, scheduling, and coordination, according to research from Stanford HAI and MIT CSAIL. Multi-Agent systems go a step further, allowing us to break down and assign tasks to specific agents with dedicated expertise. Sharing workload in this way creates a system that makes complex tasks more manageable without impacting quality. 

3. Promoting collaboration between AI and Humans 

Studies from Harvard, Wharton, and MIT Sloan reveal that highly skilled employees can enhance their performance by up to 40% using MAS, driving both organisational growth and efficiency. Given that multi-agent systems bring together specialised expertise, organisations can enhance accuracy and reduce errors compared to single, overloaded agents. The right Human-In-The-Loop (HITL) touchpoints also ensure agents are continuously learning. 

From basic AI to autonomous operations 

To benefit from AI systems, we need to identify patterns that get our agents collaborating in ways that help us achieve our goals. Doing so is key to transitioning from basic AI to autonomous AI operations. Here are three possible patterns to explore: 

1. Group chat orchestration: Simulating real-time conversation 

Group chat orchestration involves bringing multiple agents into a conversation so that they’re interacting like real users and creating a seamless and engaging flow of discussion. In this setup, each agent represents a unique participant, exchanging information, sharing updates, and keeping the discussion flowing with visibility over what other agents are saying.   

2. Hands-off: Automating sequential workflows 

Hands-off orchestration is designed for workflows where tasks are passed seamlessly from one agent to the next based on predefined rules. In this case, each agent represents a step in the workflow and focuses on completing its assigned requests before handing it off to the next.   

3. Collaborative filtering: Harnessing expertise for tailored recommendations 

Collaborative filtering orchestration keeps agents with different areas of expertise on one page to work together and provide informed recommendations. In this structure, each agent contributes with its unique insights, creating well-rounded solutions for complex user needs.  

Here’s what matters when implementing AI agents 

Successfully implementing multi-agent systems involves several steps:  

Step 1: Identifying problems and setting clear objectives  

At OneAdvanced we build (and believe in building) AI agents by gaining clarity around the problem we’re trying to solve first: Are you in need of automating repetitive tasks? Is data management your biggest pain point? This enables us to map AI to enterprise outcomes. 

Step 2: Identifying the agent/s that can address them 

At OneAdvanced we configure AI agents as digital specialists that mirror your existing team needs and support them. If you’re trying to do this independently, choosing the right agent will depend on the problem you are trying to solve. 

Below are some examples: 

• Reactive: Responds quickly to changes 
• Learning: Adapts and improves over time 
• Deliberative: Employs reasoning ability to make decisions 
• Collaborative: Excels at teamwork 
• Specialised: Manages specific task which requires unique expertise 

 Find out more about some of the types of AI agents that exist, and the ones we have on offer.  

Step 3: Selecting the right multi-agent system architectures  

Agents will follow an operating structure. At OneAdvanced we select architectures carefully to fit your workflow and needs. As covered in previous sections, some examples include: 

• Group-chat  
• Hands-off 
• Collaborative 

Step 4: Picking the right multi-agent system framework  

Instead of starting from scratch, established multi-system frameworks can help too. You should select one based on your language preference, scalability needs, and ease of use: 

• AutoGen: best for complex, regulated workflows  
• CrewAI: good for structured, team-like flows  
• LangGraph: great for regulated, audit-sensitive setups   

At OneAdvanced we evaluate frameworks so that you don’t have to – ensuring enterprise security, compliance alignment, and long-term viability. 

Step 5: Build, test, deploy and maintain the system 

This is where ideas turn into actions – the trickiest part!

The goal is to program your agents, test them to ensure they can conduct the tasks assigned, and once ready, launch them, whether in a simulation, in a real-world environment, or in a controlled testbed. 

At OneAdvanced, we realise this is complex, and we don’t just hand you the tools to build. We deliver ready-to-use AI platform and AI agents that are integrated into your sector- and function-specific workflows, with support and training built in, so that all you worry about is the result. 

Avoid common multi-agent missteps: expectations vs hype  

When deploying multiple AI agents, more isn’t always better. Without the right guidance, it’s easy to make decisions that increase complexity, cost, and risks.  

Here are some common pitfalls to consider: 

Myth 1: More agents = more intelligence 

The reality: An MIT study found 95% of generative AI projects fail due to poor collaboration within systems and workflow integration. Simply adding more agents to a system doesn’t create geniuses. Without a proper framework, protocols, collaboration and defined goals, increasing the number of agents can lead to chaos and failed outcomes.  

Hype 2: Multi-agents require no human intervention.  

The reality:  Despite the growing excitement around multi-agent systems, most deployments still rely on human input to monitor and guide systems. Human oversight remains crucial. It ensures reliability, safety, and ethical decision-making. 

Myth 3: Multi-agents can handle all complex workflow 

The reality: Multi-agent systems excel in structured, well-defined tasks. With workflows characterised by ambiguity, conflicting objectives, or the need for long-term memory, their performance declines and they can start hallucinating or get stuck in repetitive loops. 

Myth 4: Multi-agent mimic human-like teamwork  

The reality: Right now, even the best LLMs require an expert to assess whether the output is correct or not. Unlike humans, agents don’t share emotional intelligence, ethical standards, or adaptability so they make mistakes of their own. True teamwork requires shared understanding and collaboration between humans and agents to reap the greatest benefits.  

Transform your workflows with OneAdvanced AI agents 

At OneAdvanced, we’re helping power your world of work with our newly launched AI agents. Tailored to meet your unique sector and functional needs, our AI agents seamlessly integrate into your workflows to deliver tangible enterprise value. Our over 25 years of experience automating workflows ensures we have you covered, without having to worry about any of the above yourself. Explore our AI agents today and request more information! 

Frequently Asked Questions (FAQs) 

How do multi-agent systems support enterprise transformation? 

Multi-agent systems drive enterprise transformation by enabling AI agents to collaborate, automate complex tasks, improve decision-making, and adapt seamlessly across interconnected functions. 

What challenges come with high and full-autonomy systems? 

Fully or high autonomous systems lack human control, which poses significant challenges, such as navigating ethical dilemmas, ensuring consistent reliability in dynamic and complex environments, obtaining regulatory approval, and addressing public trust concerns. 

How should you plan your transition toward agentic systems? 

Planning a transition towards an agentic world requires a strategic approach which includes creating a clear roadmap, ensuring collaboration between diverse teams, and adopting iterative deployment to manage risk and optimise adoption.