Agentic artificial intelligence (AI) is the new revolution. With its ability to merge numerous agents into a single, organized ecosystem that autonomously handles real-time queries, it can transform business processes — from adjusting retail supply chain inventory to reconfiguring manufacturing lines to meet fluctuating demand.
Supporting the agentic AI powerhouse is a big ask! To unleash agentic AI’s full capabilities requires multiple data sources that are actively integrated across a global reach. This article shows how the event-driven approach, nurtured through an agent mesh, is the foundation for building an agentic AI powerhouse.
The game of AI is rapidly changing, and agentic AI is the new hierarchy. Early gateway prototype applications using retrieval-augmented generation (RAG) and large language models (LLMs) have been quickly surpassed. Agentic AI’s innovative programming allows it to process systems independently without prompted guidance while adapting to new circumstances.
The Transformative Art of Agentic AI
The evolution of agentic frameworks has undergone a remarkable transformation. Initially, these systems were limited to rule-based tasks. They have now steadily evolved into sophisticated, multimodal agents.
Agentic AI goes beyond simple tasks such as question-and-answer exchanges for which LLMs are primarily trained. It uses multiple LLMs and services, aka ‘agents’ to perform more complex tasks and reasoning autonomously.
These agents possess the ability to process and integrate information from diverse sources, including text, images and audio. This multimodality empowers AI agents with reasoning capabilities that can interact in ways that can almost simulate human understanding.
For example, agentic AI could act as a customer support agent for any task, such as in the case of a new ticket, for finding similar tickets and answering product usage questions mentioned in the ticket, and finally adding its findings in the form of comments in the ticket. This opens up the ability to tackle a huge spectrum of business challenges from intuitive customer communications, real-time decisions and fleet management.
The Agentic AI Pilot has Boarded, but the Landing Gear Isn’t Prepared
Getting out of the pilot phase and into everyday business applications is proving to be the biggest hurdle for any AI project. HBR has estimated that AI projects have a failure rate of up to 80%. According to an IBM study of over 8,500 IT professionals across the globe, the top barriers for AI deployments were attributed to limited AI skills and expertise, data complexity and ethical concerns.
In addition, other studies show many projects fail to scale not only due to legacy architecture dependencies, but also costs and performance issues in scaling something so complex and unstructured. Even when projects succeed in commencing operations, data quality, governance, security and tech workflow integration hurdles remain.
Introducing the Powerhouse AI: An Event-Driven Structure and Mesh
At the heart of these challenges lies a critical deficiency — the absence of real-time, contextual information flow. Traditional batch processing and static data models still in use by many organizations fall short of providing dynamic business environments where decisions, often made in split seconds, if you consider financial trading, are the make or break of trading opportunities.
An event mesh, underpinned by event-driven architecture (EDA), is the missing ingredient that promises to transform enterprise AI into a real-time, context-aware powerhouse. An event mesh is an interconnected network of event brokers that dynamically route event-driven information between all kinds of applications and devices across environments and around the world.
Here’s where the event mesh shines for AI deployment: It provides the decoupling required for rapid development and change, and it delivers on the event-driven architecture that allows for managing rate mismatch, thus supporting different applications with varied messaging patterns and delivering the efficiency needed to scale horizontally as well as vertically.
When you apply the architectural pattern enabled by the event mesh across Agentic AI use cases, you essentially create a flexible, real-time data distribution network that enables various AI models to access and react to relevant data streams instantly.
A Clean Casing of Agent Mesh
While an event mesh enables real-time data flow and dynamic routing across the enterprise, an agent mesh takes this further by introducing intelligent agents that can autonomously reason about and act on this information flow.
An agent mesh is a framework that allows you to build a network of AI agents overseen and controlled by a dynamic orchestration layer, allowing complex tasks to use multiple agents and bring their results together in a data management system. Agent mesh gateways allow access to this system for many different use cases, each with its own input interface and authorization type.
Essentially, organizations can enable truly autonomous Agentic AI systems that can manage requests to deliver the best results based on unstructured inputs, such as chats.
A Firm AI Architecture Ensures Businesses can Taper Integration
Best of all, an agent mesh is not intrusive to an organization’s existing application stack and Agentic AI framework. With its ‘plug-and-play’ style approach, organizations can start small with one or two use cases and gradually let the agent mesh evolve by adding agents to expand its capabilities, as well as new agent mesh gateways to add further use cases and interfaces to the system.
Incrementing Space for Advancement Opportunities
With orchestration and built-in access control of all agents and actions in the system, one framework can be used and reused for many use cases — be it a new order, a new support ticket or even a question from a chatbot — each providing different interfaces and access controls that are governed by enterprise-grade security.
In a landscape where AI technologies are rapidly evolving, the decoupled nature of an event-driven framework underpinning agent mesh allows organizations to easily update, replace or add new AI models and data sources without disrupting existing systems. This is especially crucial for staying current with AI advancements.
AI Agents’ Prospective Outlook Is Equipped With an Armory of Agent Mesh
Traditional LLM models are no longer the frontrunners. Agentic AI is reaching new heights when it comes to the use of AI-embedded systems. To reach the full capacity of Agentic AI in overseeing warehouse inventories or even altering supply chain levels autonomously, a real-time, detailed stream of information is essential.
It’s where an agent mesh is essential for unlocking the full value of these AI agents in fast-changing business environments.
