AI Publications

Introduction

Today’s push to deploy autonomous, agentic AI systems may be repeating a well-known mistake from the early days of microservices. What was once heralded as a path to agility and scalability ended up producing brittle, interdependent systems — “distributed monoliths” — that proved fragile and hard to maintain.

Unless architects learn from that history, agentic AI risks falling into the same trap. The approach has to be different, instead of linking agents with synchronous, point-to-point calls, build them on an event-driven, loosely coupled architecture, enabling resilience, scalability, observability and long-term growth.

The microservices déjà vu

When microservices first became popular, the idea was to break big monolithic applications into smaller, more modular services. Each service could evolve and scale independently. In theory this promised flexibility, faster deployment cycles, and easier maintainability. But in practice many teams implemented microservices with synchronous API calls — service A calling service B, which in turn called service C, and so on.

That wiring created hidden dependencies: even though each component was separate, the system behaved like a tightly coupled monolith. If one service slowed down or failed, it could cascade and bring down the entire system. Releases required orchestration across dozens of interdependent modules, and debugging a single issue could take days.

In short: modularity on paper did not mean modularity in practice. The architecture looked distributed — but behaved as a monolith. The lesson: “true independence requires loose coupling.”

Agentic AI is falling into the same trap

Modern “agentic” systems — those built around multiple autonomous agents — are often connected using the same synchronous, tightly integrated patterns that doomed early microservices.

For example, an enterprise AI assistant may consist of separate agents: one for sentiment analysis, another for knowledge retrieval, a reasoning agent, and a response-generation agent. But if each depends on the previous one finishing exactly before it can run, the system becomes brittle. Real-world conditions — load spikes, latency, resource contention — easily cause delays or failures, which ripple through the chain. Updates require coordinating multiple agents together. The illusion of modularity quickly collapses under operational stress.

That undermines the very promises that make agentic AI appealing — scalability, flexibility, adaptability. Instead, teams risk re-creating a “distributed monolith,” with associated scalability, reliability, and maintenance challenges.

Loosen up: Event-driven AI systems

To avoid repeating the microservices mistakes, make sense adopting an event-driven architecture (EDA) for agentic AI. In this paradigm, agents are decoupled from each other by a central “event broker” — a kind of message hub. Rather than calling each other directly, agents publish events when something happens (“customer inquiry received”, “analysis done”), and other agents subscribe to relevant event types and react when they see matching events.

This shift — from synchronous, request/response style to asynchronous, event-driven messaging — brings major advantages. First, resilience: if one agent fails or is under heavy load, its events queue up rather than crashing the whole system. Second, scalability: to increase capacity, you can simply add more agents to consume from the same event streams. Third, flexibility and independent evolution: different teams can build, update, and deploy agents independently, even using different technologies — without risking system-wide disruption.

Designed for asynchronous reality

One of the fundamental characteristics of agentic AI systems is that they are inherently asynchronous. Agents often rely on large language models whose response times vary depending on model load and complexity. Some tasks may finish in seconds, others in minutes. And if human approval or review is part of the workflow (for instance, to sign off on a transaction or verify a recommendation), the timing becomes unpredictable.

An event-driven architecture is naturally aligned with that asynchronous reality. Rather than blocking — waiting for one agent to finish before starting the next — agents can publish and subscribe to events as they complete tasks. Multiple agents operate concurrently: one could be analyzing sentiment, another retrieving relevant data, a third drafting a reply — all in parallel, and without interfering with each other.

This kind of asynchronous workflow is not just a matter of elegance, but a practical necessity for real-world scalability and robust user experience. It accommodates variability, latency, and human-in-the-loop delays — avoiding system bottlenecks that would cripple a synchronous design.

Dynamic, observable, and compliant

The adoption of EDA for agentic AI brings additional important advantages beyond resilience and scalability — namely observability, traceability, and compliance. Because all interactions are captured as events, with context and timestamps, organizations gain end-to-end visibility into what the AI system did, when, and why. That means debugging becomes easier, auditing becomes feasible, and for regulated industries, compliance with data-handling or decision-tracking requirements becomes realistic.

Moreover, EDA supports dynamic adaptability. When a new agent is introduced — for example, a specialized model to handle legal document review — it simply “subscribes” to the event streams that are relevant to its domain. There’s no need to rewrite orchestration logic or redeploy the entire system. The system can evolve — add or retire agents — with minimal friction, allowing the enterprise to grow capabilities over time.

This dynamic and observable infrastructure lays the groundwork not only for technical scalability, but also for business scalability: as workflows grow more complex, or regulations evolve, or audit demands increase, the architecture supports flexible evolution rather than brittle rigidity.

Building AI that scales with your business

Agentic AI — autonomy, efficiency, scalability, end-to-end automation — can only be realized if architectures are designed from the ground up to support independence, resilience, and evolution. Trying to “bolt on” agentic behavior atop monolithic or tightly coupled systems almost guarantees failure.

Adopting an event-driven mesh architecture gives enterprises a solid foundation for growth. Agents remain loosely coupled, coordinate through events rather than direct calls, and can be composed, added, removed, or replaced without putting the whole system at risk. The system becomes dynamic, scalable, and maintainable — capable of evolving as business needs evolve.

Conclusion

The rise of agentic AI represents a transformative moment: AI agents promise to automate complex tasks, make decisions, coordinate across systems — and potentially unlock tremendous efficiency and innovation. But we risk repeating the mistakes of the microservices era. Without careful architectural design, today's promising systems can devolve into brittle, inflexible distributed monoliths.

The good news: there is a path forward. By embracing event-driven architecture — decoupled agents coordinating via event brokers, operating asynchronously, with full visibility and traceability — organizations can harness the full potential of agentic AI while avoiding the pitfalls. Building scalable, dynamic, observable, and compliant AI systems starts with architecture. Those who adopt this approach early will be best positioned to scale responsibly and innovate sustainably in the age of intelligent automation