AI agents are set to transform enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. But the real challenge isn’t building better models.
Agents need access to data, tools, and the ability to share information across systems, with their outputs available for use by multiple services—including other agents. This isn’t an AI problem, it’s an infrastructure and data interoperability problem. It requires more than stitching together chains of commands; it demands an event-driven architecture (EDA) powered by streams of data.
As HubSpot CTO Dharmesh Shah recently put it, “Agents are the new apps.” Meeting this potential requires investing in the right design patterns from the start. This article explores why EDA is the key to scaling agents and unlocking their full potential in modern enterprise systems.
The Rise of Agentic AI
While AI has come a long way, we’re hitting the limits of fixed workflows and even LLMs.
Google’s Gemini is reportedly failing to meet internal expectations despite being trained on a larger set of data. Similar results have been reported by OpenAI and their next-generation Orion model.
Salesforce CEO Marc Benioff recently said on The Wall Street Journal’s “Future of Everything” podcast that we’ve reached the upper limits of what LLMs can do. He believes the future lies with autonomous agents—systems that can think, adapt, and act independently—rather than models like GPT-4.
Control logic, programmatic versus agentic (Source: Sean Falconer)
Agents bring something new to the table: dynamic, context-driven workflows. Unlike fixed workflows, agentic systems figure out the next steps on the fly, adapting to the situation at hand. That makes them ideal for tackling the kinds of unpredictable, interconnected problems businesses face today.
Agents flip traditional control logic on its head.
Instead of rigid programs dictating every move, agents use LLMs to drive decisions. They can reason, use tools, and access memory—all dynamically. This flexibility allows for workflows that evolve in real time, making agents far more powerful than anything built on fixed logic.
Agent architecture (Inspired by https://ift.tt/ZqkYR5g)
The Challenges with Scaling Intelligent Agents
Scaling agents—whether a single agent or a collaborative system—hinges on their ability to access and share data effortlessly. Agents need to gather information from multiple sources, including other agents, tools, and external systems, to make decisions and take action.
Single agent dependencies (Source: Sean Falconer)
Connecting agents to the tools and data they need is fundamentally a distributed systems problem. This complexity mirrors the challenges faced in designing microservices, where components must communicate efficiently without creating bottlenecks or rigid dependencies.
Like microservices, agents must communicate efficiently and ensure their outputs are useful across the broader system. And like any service, their outputs shouldn’t just loop back into the AI application—they should flow into other critical systems like data warehouses, CRMs, CDPs, and customer success platforms.
Sure, you could connect agents and tools through RPC and APIs, but that’s a recipe for tightly coupled systems. Tight coupling makes it harder to scale, adapt, or support multiple consumers of the same data. Agents need flexibility. Their outputs must seamlessly feed into other agents, services, and platforms without locking everything into rigid dependencies.
What’s the solution?
Loose coupling through an event-driven architecture. It’s the backbone that allows agents to share information, act in real time, and integrate with the broader ecosystem—without the headaches of tight coupling.
Event-Driven Architectures: A Primer
In the early days, software systems were monoliths. Everything lived in a single, tightly integrated codebase. While simple to build, monoliths became a nightmare as they grew.
Scaling was a blunt instrument: you had to scale the entire application, even if only one part needed it. This inefficiency led to bloated systems and brittle architectures that couldn’t handle growth.
Microservices changed this.
By breaking applications into smaller, independently deployable components, teams could scale and update specific parts without touching the whole system. But this created a new challenge: how do all these smaller services communicate effectively?
If we connect services through direct RPC or API calls, we create a giant mess of interdependencies. If one service goes down, it impacts all nodes along the connected path.
Tightly-coupled Microservices (Source: Sean Falconer)
EDA solved the problem.
Instead of tightly coupled, synchronous communication, EDA enables components to communicate asynchronously through events. Services don’t wait on each other—they react to what’s happening in real-time.
Event-Driven Architecture (Source: Sean Falconer)
This approach made systems more resilient and adaptable, allowing them to handle the complexity of modern workflows. It wasn’t just a technical breakthrough; it was a survival strategy for systems under pressure.
The Rise and Fall of Early Social Giants
The rise and fall of early social networks like Friendster underscore the importance of scalable architecture. Friendster captured massive user bases early on, but their systems couldn’t handle the demand. Performance issues drove users away, and the platform ultimately failed.
On the flip side, Facebook thrived not just because of its features but because it invested in scalable infrastructure. It didn’t crumble under the weight of success—it rose to dominate.
Today, we risk seeing a similar story play out with AI agents.
Like early social networks, agents will experience rapid growth and adoption. Building agents isn’t enough. The real question is whether your architecture can handle the complexity of distributed data, tool integrations, and multi-agent collaboration. Without the right foundation, your agent stack could fall apart just like the early casualties of social media.
The Future is Event-Driven Agents
The future of AI isn’t just about building smarter agents—it’s about creating systems that can evolve and scale as the technology advances. With the AI stack and underlying models changing rapidly, rigid designs quickly become barriers to innovation. To keep pace, we need architectures that prioritize flexibility, adaptability, and seamless integration. EDA is the foundation for this future, enabling agents to thrive in dynamic environments while remaining resilient and scalable.
Agents as Microservices with Informational Dependencies
Agents are similar to microservices: they’re autonomous, decoupled, and capable of handling tasks independently. But agents go further.
While microservices typically process discrete operations, agents rely on shared, context-rich information to reason, make decisions, and collaborate. This creates unique demands for managing dependencies and ensuring real-time data flows.
For instance, an agent might pull customer data from a CRM, analyze live analytics, and use external tools—all while sharing updates with other agents. These interactions require a system where agents can work independently but still exchange critical information fluidly.
EDA solves this challenge by acting as a “central nervous system” for data. It allows agents to broadcast events asynchronously, ensuring that information flows dynamically without creating rigid dependencies. This decoupling lets agents operate autonomously while integrating seamlessly into broader workflows and systems.
An Event-Driven Architecture for Agents (Source: Sean Falconer)
Decoupling While Keeping Context Intact
Building flexible systems doesn’t mean sacrificing context. Traditional, tightly coupled designs often bind workflows to specific pipelines or technologies, forcing teams to navigate bottlenecks and dependencies. Changes in one part of the stack ripple through the system, slowing innovation and scaling efforts.
EDA eliminates these constraints. By decoupling workflows and enabling asynchronous communication, EDA allows different parts of the stack—agents, data sources, tools, and application layers—to function independently.
Take today’s AI stack, for example. MLOps teams manage pipelines like RAG, data scientists select models, and application developers build the interface and backend. A tightly coupled design forces all these teams into unnecessary interdependencies, slowing delivery and making it harder to adapt as new tools and techniques emerge.
In contrast, an event-driven system ensures that workflows stay loosely coupled, allowing each team to innovate independently.
Application layers don’t need to understand the AI’s internals—they simply consume results when needed. This decoupling also ensures AI insights don’t remain siloed. Outputs from agents can seamlessly integrate into CRMs, CDPs, analytics tools, and more, creating a unified, adaptable ecosystem.
Scaling Agents with Event-Driven Architecture
EDA is the backbone of this transition to agentic systems.
Its ability to decouple workflows while enabling real-time communication ensures that agents can operate efficiently at scale. As discussed here, platforms like Apache Kafka exemplify the advantages of EDA in an agent-driven system:
Horizontal Scalability:
Kafka’s distributed design supports the addition of new agents or consumers without bottlenecks, ensuring the system grows effortlessly.
Low Latency:
Real-time event processing enables agents to respond instantly to changes, ensuring fast and reliable workflows.
Loose Coupling:
By communicating through Kafka topics rather than direct dependencies, agents remain independent and scalable.
Event Persistence:
Durable message storage guarantees that no data is lost in transit, which is critical for high-reliability workflows.
Agents as event producers and consumers on a real-time streaming platform (Source: Sean Falconer)
Data streaming enables the continuous flow of data throughout a business. A central nervous system acts as the unified backbone for real-time data flow, seamlessly connecting disparate systems, applications, and data sources to enable efficient agent communication and decision-making.
This architecture is a natural fit for frameworks like Anthropic’s Model Context Protocol (MCP).
MCP provides a universal standard for integrating AI systems with external tools, data sources, and applications, ensuring secure and seamless access to up-to-date information. By simplifying these connections, MCP reduces development effort while enabling context-aware decision-making.
EDA addresses many of the challenges MCP aims to solve. MCP requires seamless access to diverse data sources, real-time responsiveness, and scalability to support complex multi-agent workflows. By decoupling systems and enabling asynchronous communication, EDA simplifies integration and ensures agents can consume and produce events without rigid dependencies.
Event-Driven Agents Will Define the Future of AI
The AI landscape is evolving rapidly, and architectures must evolve with it.
And businesses are ready. A Forum Ventures survey found that 48% of senior IT leaders are prepared to integrate AI agents into operations, with 33% saying they’re very prepared. This shows a clear demand for systems that can scale and handle complexity.
EDA is the key to building agent systems that are flexible, resilient, and scalable. It decouples components, enables real-time workflows, and ensures agents can integrate seamlessly into broader ecosystems.
Those who adopt EDA won’t just survive—they’ll gain a competitive edge in this new wave of AI innovation. The rest? They risk being left behind, casualties of their own inability to scale.
About the author: Sean Falconer is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Sean also hosts the popular engineering podcasts Software Engineering Daily and Software Huddle. You can connect with Sean on LinkedIn.
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