Artem Agaev, an expert in high-load backend architecture working at a leading financial institution in Eastern Europe, explains what companies have to fix behind the scenes before AI can work reliably at scale
AI readiness depends on the enterprise systems behind them: weak integration layers can prevent data, workflows, and intelligence from moving at business speed, according to IBM’s 2026 analysis. For large companies, this turns backend modernization into a core business challenge. Many have already digitized key operations, but their systems still need to handle growing data volumes, real-time user expectations, AI workloads, API connections, event streams, and partner integrations without slowing releases or putting critical services at risk.
Artem Agaev, a software engineer with eight years of experience building high-load, data-intensive backend systems in HR tech, suggests treating enterprise AI readiness as a full-cycle architecture modernization. At one of the region’s largest financial institutions, he modernized a high-load backend system processing more than 400 GB of data daily; delivered an ETL service that prepared large data streams for AI model processing; built a daily-content backend service capable of handling up to 300,000 requests per day; and launched a centralized document-signing service scaled to more than 230,000 users. He also conducts code reviews across a development cluster of about 30 engineers, consults colleagues, and mentors junior developers. He is a Hackathon Raptors Fellow and served on the jury of the American Business Expo Xmas Award 2025, evaluating projects in software development and data management.
The challenge becomes clearer in several backend problems that enterprise platforms face as they move toward AI- and data-driven operating models.
1/ The old monolith blocks growth, but a rushed microservice migration can create bigger risks
Legacy monoliths often survive for a good reason. They run important business processes, contain years of embedded logic, and support services that cannot simply be stopped while engineers rebuild them. The problem begins when the system becomes too rigid for the business around it. As Artem shares,
“A new feature takes too long. One module cannot be scaled separately from the rest. A small change requires regression testing across the entire application. Integrations become slower and more expensive because nobody fully understands the hidden dependencies inside the codebase.”
Large banks, industrial platforms, public-sector systems, and enterprise HR platforms face this issue frequently. The system is already mission-critical, but its architecture no longer matches the speed of the business.
Some companies try a full rewrite, hoping to replace the old system with a clean new platform. That path can become expensive and dangerous because the new system has to reproduce years of business rules before it can deliver value. Others split the monolith into services too quickly, treating microservices as a goal in themselves. That can produce a distributed version of the same confusion: more services, more network calls, more operational complexity, but not necessarily more control.
Artem’s production experience led him to a more cautious view of microservice migration. Before writing about the topic, he had worked with backend systems where migration was a live production challenge involving high data volumes, AI-related processing, recommendation logic and services that had to remain stable under load. Through this experience, he saw the risk a monolith may slow development, but a poorly planned split can create even more fragile dependencies, unclear service ownership, and harder failure control.
He later developed this approach in a recent article published in the International Journal of Advanced Engineering, Management and Science, an international open-access, peer-reviewed journal. In the article, he examined migration from monolithic systems to microservice architecture through domain boundaries, service identification, modular monoliths and architectural trade-offs in complex software environments.
“Before cutting the system into services, engineers need to understand domain boundaries, data structures, dependency chains, operational limits, and the quality of existing artifacts. The intermediate stage may involve a modular monolith, pattern-based migration, AI or ML tools for detecting hidden dependencies, and expert review of architectural decisions,” he explains.
Modernization is not just moving code into separate services. Microservices help only when service boundaries follow business logic, data ownership is clear, latency is accounted for, communication is deliberate, and teams can own services independently. Without that discipline, migration simply redistributes complexity.
AI workloads make this discipline more important. They need reliable data access, stable interfaces, and predictable processing flows. If AI features sit on top of a poorly understood legacy backend, they inherit its weak points. A careful migration reduces that risk and creates a stronger base for automation and integration.
2/ A system can become scalable but still difficult to develop
Even after a company moves beyond a monolith, another problem appears. A distributed backend may handle more traffic and still be hard to maintain. The problem starts when business rules become tangled with databases, brokers, frameworks, APIs, and transport protocols. More tests and stricter code review help, but they cannot fix an architecture that fails to separate the product’s stable core from changing infrastructure.
“This is especially costly in enterprise systems, where business rules change constantly: approval flows, document processes, permissions, recommendations, reporting, and compliance requirements all evolve. AI adds more moving parts through data pipelines and model-processing flows,” Artem says. “If each change cuts across too many technical layers, the product slows down even when the system can still handle traffic.”
Artem addressed this problem from another angle in an article published in the Australian Journal of Science and Technology, a quarterly international open-access peer-reviewed journal published by Melbourne Scientific Publishers. Drawing on his experience with high-load backend systems, AI-related data services and enterprise platforms where core workflows must keep running as infrastructure evolves, he examined clean architecture as a practical discipline for scale. In this model, business logic and use cases remain at the center of the system, while databases, queues, web frameworks, transport protocols and external services stay in replaceable outer layers. That separation makes the backend easier to extend, test and modernize without forcing every technical change to disturb the product’s core behavior.
In backend systems, clean architecture creates a clear boundary between product logic and technical plumbing. The domain layer explains what the product does; infrastructure handles how it connects to databases, queues, APIs, and frameworks. Those components can then change without forcing teams to rewrite the core logic each time.
Clean architecture works as a scaling tool. It helps a backend grow across load, functionality, and team size: developers know where business logic belongs, new services can be added with controlled dependencies, and AI or data-processing changes are less likely to disrupt critical workflows. This is why Artem treats it as part of enterprise backend modernization rather than a purely internal engineering practice.
At this point, architecture starts to affect business execution. Faster releases, fewer failures, smoother onboarding, and lower technical debt shape how quickly a company can launch products, meet regulatory changes, personalize services, and connect new digital channels.
3/ Microservices scale the system, but they also expose the communication layer
Microservices solve some problems and create others. Once a system becomes distributed, reliability depends not only on the strength of each service, but on the quality of communication between services. A failure in one service, gateway, broker, API, or transport layer can spread across the platform if interactions are not designed with failure in mind.
This is one of the main risks for modern AI- and data-intensive platforms. They depend on many moving parts: data ingestion, model processing, recommendation logic, user-facing interfaces, background jobs, access controls, notifications, document workflows, analytics, and third-party integrations. When the number of interactions grows, resilience becomes an architectural property rather than an infrastructure add-on.
Artem suggests designing fault tolerance as a property of the whole platform, not as a backup measure added after deployment. That means combining decomposition, asynchronous communication, replication, orchestration, monitoring loops and automated recovery so that a local failure does not become a system-wide outage. If one component becomes unavailable, a request can be retried, a task can wait in a queue, traffic can shift to a replica, and recovery can begin before users or business operations are seriously affected.
He developed this resilience-focused approach in a recent article on architectural patterns for fault-tolerant microservice systems. The article continues the same practical argument: once enterprise platforms become distributed, reliability has to be designed into communication, recovery and service interaction, not treated as an operational afterthought.
For companies running AI pipelines, this approach is particularly important. AI workloads can be irregular. Some processes are batch-based, others are real-time. Some depend on large data streams, while others depend on fast inference. A stable communication layer allows the platform to absorb this complexity. Without it, the system may scale in theory but become unreliable in practice.
Artem Agaev’s engineering work and research make a clear case: backend modernization now means building systems strong enough to carry AI workloads, large data flows and constant product change without breaking under pressure. His articles turn that production experience into a practical framework for enterprise platforms.
He brought this perspective to a broader professional discussion as a jury member of the American Business Expo Xmas Award 2025, where he evaluated projects in software development, data management and architecture, and technology leadership. Among that year’s laureates were a cloud-native lakehouse architecture for regulated invoice automation and an LLM monitoring system designed to control neural network performance in real time.
“I came away from that exchange with a sense that AI and data-driven products are now being judged by the engineering discipline behind them. The strongest platforms will be those that move data predictably, contain failures, protect core business logic, and keep critical workflows stable as workloads and integrations grow,” Artem says.
That is the larger test now facing enterprise technology teams. Backend modernization is no longer a cleanup project hidden inside engineering departments. It is becoming the condition for whether companies can safely expand AI, personalize services, connect more systems, and keep releasing new functions without destabilizing production. Artem Agaev’s work points to this shift: architecture is becoming the measure of whether digital ambition can survive real operating pressure.
