Agentic AI to iGentic – How Multi Agent Orchestration is Refining Enterprise AI?

Introduction 

AI has been around since the 1950s, yet the large-scale adoption of Machine Learning models and ML platforms from Azure, AWS, and Google came in only by 2015. Leveraging deep learning ML models as its foundation, Gen AI was born.  

While everyone started using Gen AI, the question still remained around how Gen AI impacts business outcomes. I believe Agentic AI is a big step towards addressing this gap and creating powerful solutions that can improve ROI, increase Operational Efficiency, and mitigate business risks for organizations. 

At a fundamental level, it’s easy to look at agentic as a single agent talking to a foundational model to create some outcomes. However, we understand that enterprise solutions often run with complex architectures and services that orchestrate together to create meaningful enterprise outcomes. The same is true for Agentic AI as well. 

The Rise of Multi Agent Orchestration Platforms 

How do we run a multi-agentic solution that is scalable, flexible, and can orchestrate efficiently within the enterprise context? That question led us to build “identity.” Our goal was to achieve a high degree of efficient collaboration between agents while at the same time ensuring that the agents cross-collaborate across clouds.  

With the global Agentic AI market projected to surge from $5.2 billion (2024) to $196.6 billion by 2034, organizations are prioritizing multiagent orchestration platforms to bridge the gap between experimentation and ROI. 

iGentic – The Future of Autonomous Enterprise 

A platform like iGentic combined with the right agentic architectural design patterns is the recipe for enterprises to drive successful business outcomes with AI. From automating customer services to optimizing complex logistics, iGentic multi-agent orchestration platform empowers businesses to build specialized AI agents tailored to unique needs. You can build, orchestrate, and deploy intelligent AI agents that seamlessly integrate with your existing systems. 

Let us briefly look at what are these design patterns around Agentic AI: 

1. Reflection – Agents use mechanisms like self-evaluation and feedback loops to introspect and iteratively improve the results by refining their approach. 
2. Tool Use – As the name implies, agents use tools to delegate and perform specialized tasks. I love this – think about all services in an enterprise that can be leveraged by an agent. 
3. Planning – Agents create multi-step plans based on scenarios, constraints, and goals. This is where agents differ from a traditional RPA. Dynamic workflows – driven by goal alignment! 
4. Multi-Agent Pattern – How about the division of labor, orchestration, and single responsibility principle? Microservices – anyone? 

Let us not start by combining design patterns, and you get where the wind is drifting. Until next time, take it easy! 

The Path Forward: Transforming Enterprise Operations 

Multi-agent orchestration represents a seismic shift in enterprise operations, moving from connected systems to intelligent autonomous workflows that span entire organizations. Early adopters of multi agent systems are already experiencing significant improvements in terms of operational efficiency, customer satisfaction, and cost savings. 

Ready to unlock the power of multi-agent orchestration? 

Discover more about iGentic Platform!