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

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.