Agentic AI Needs Event-Driven Thinking to Unlock its Full Potential

Edward Funnekotter, the Chief AI Officer at Solace, predicts that Agentic AI will be a significant change for businesses but believes it will require event-driven thinking to unlock its full potential. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Agentic AI is the new kid on the AI block. It is a huge leap forward from simple LLM applications by combining multiple agents into a system that can act autonomously to answer a question or achieve a result, such as optimizing retail inventory in real-time or adjusting production lines to meet demand fluctuations. But to unlock its full potential to deliver applications that can think and reason to solve these complex and mind-bending problems means data from multiple sources must be integrated in real-time and on a global scale. An event-driven approach manifested through an agent mesh is essential for intelligent, adaptive Agentic AI systems to respond to the complex demands of modern business.

We’re moving fast up the AI transformational staircase, from entry-level applications directly using Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) to the much-heralded Agentic AI – an AI system that doesn’t just follow pre-programmed instructions, but thinks on its feet, makes decisions, and adapts to new situations.

In the words of Gartner in its strategic technology trends for 2025: “Agentic AI systems autonomously plan and take actions to meet user-defined goals. Agentic AI promises a virtual workforce that can offload and augment human work. Gartner predicts that by 2028, at least 15 percent of day-to-day work decisions will be made autonomously through Agentic AI.”

The Value of Agentic AI for Business

Agentic AI goes far beyond simple question-answering on a subject for which an LLM has been trained. It is a software pattern that uses multiple LLMs and services, aka “agents,” to perform more complex tasks and reasoning autonomously. The evolution of agentic frameworks has seen a remarkable transformation. Initially, these systems were limited to rule-based tasks. They have now steadily advanced into sophisticated, multimodal agents.
These agents can 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 almost simulate human understanding. They are like flexible and adaptable employees with a specific area of expertise.

For example, it could act as a customer support agent with the task of “finding similar tickets for each new ticket and answering product usage questions in the ticket. Then add your findings as a comment to the ticket.” This opens up the ability to tackle a huge spectrum of business challenges, including intuitive customer communications, real-time inventory decisions, fleet management, and more.

Don’t jump the gun—agentic AI projects are in the starting blocks, but the pistol needs to be fired.

That said, 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 as high as 80 percent. In fact, according to an IBM study of over 8,500 IT professionals worldwide, limited AI skills and expertise, data complexity, and ethical concerns were cited as top barriers to AI deployments.

In addition, other studies show that many projects fail to scale due to legacy architecture dependencies and the costs and performance involved in scaling something so complex and unstructured. Even when projects get up and running, data quality, governance, security, and tech workflow integration hurdles remain.

Meet the AI Force Multiplier: Event-Driven Architecture and the Event 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 that have to be 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 routes 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 needed for rapid development and change, and it delivers on the event-driven architecture that allows for managing rate mismatch, supporting different applications with messaging patterns, and delivering the efficiency needed to scale horizontally and 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.

And Now Meet the 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. This allows complex tasks to use multiple agents and combine their results in a data management system. Agent mesh gateways enable access to this system for many different use cases, each with its own type of input interface and authorizations. 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 flexible, composable AI framework means organizations can pace themselves…

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 then evolve the agent mesh over time by adding agents to increase its capabilities and new agent mesh gateways to add further use cases and interfaces to the system.

…then evolve in lockstep with business growth

Then, with orchestration and built-in access control of all agents and actions in the system, one framework can be used and re-used 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 control 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.

The Future of AI Agents Will Include an Agent Mesh

Agentic AI is a sea change in the use of AI, going beyond simple LLM applications to create autonomous systems capable of never-before-seen levels of reasoning and adaptation. To realize its full potential to dynamically manage inventory levels in a warehouse or reconfigure supply chains on the fly, agentic AI must address its need for real-time, contextual information flow. This is where the agent mesh will become the key to maximizing the value of AI agents in these dynamic business environments.