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Migration Patterns: From Legacy Systems to AI-Enabled Architecture

For years, enterprise systems have been modernized in familiar ways. A monolith becomes services. A database becomes several databases. Synchronous calls become events. Batch processing becomes near-real-time processing. Older interfaces are replaced with APIs. Infrastructure moves to newer platforms. The…

  • Software Architecture

When Unit Tests Were Not Enough for AI Agents

For years, we knew how to test software. A method receives an input. It produces an output. We write a test. For a traditional service, that model works remarkably well. We test business rules. We test APIs. We test database…

  • Software Architecture

When Multiple AI Agents Started Behaving Like a Distributed System

A user asks: “Review this supplier invoice, verify that everything looks correct, and prepare it for payment.” From the user’s perspective, this is one request. But imagine the system decides to use several AI agents: At first glance, this looks…

  • Software Architecture

When an AI Agent Crossed Five Transaction Boundaries

A user asks an AI agent: “Pay this invoice, update the records, and let the supplier know when it’s done.” It sounds like one business operation. From the user’s perspective, it is one request. From the system’s perspective, it may…

  • Software Architecture

The LLM Boundary: Where AI Should Stop in Financial Systems

A customer asks: “How much can I safely spend today?” An LLM can understand the question. It can explain the customer’s account. It can summarize recent transactions. It can even reason about what information might be relevant. But there is…

  • Software Architecture

When an AI Agent Became a Distributed System

For years, we learned to think about distributed systems in a particular way. A request enters a service. The service calls another service. That service may call a database, a message broker, a payment provider, or another internal system. Somewhere…

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