Why AI Applications Are Becoming Distributed Systems

Historically, AI applications have been relatively straightforward. A user submits a prompt, an application transmits that prompt to a model, the model responds with an answer, and the application displays it. However, modern AI applications are undergoing a significant transformation, increasingly incorporating web searches, external API calls, tool execution, database interactions, multiple model executions, background task handling, state maintenance, and even delegating work to other AI agents.

This shift constitutes one of the most critical architectural changes in software engineering today.

Google Cloud's exploration of distributed AI agents and OpenAI's agent guidance illustrate architectures where specialized agents operate as separate services and communicate through orchestration layers. These architectural changes occur even when developers unintentionally adopt a distributed system.

Consider a basic AI-powered application: User | Frontend | Backend | LLM API | Response. This simple architecture includes challenges related to latency, cost, authentication, rate limits, and error handling. But now, imagine adding real-world capabilities such as web search, document reading, database querying, external API calls, memory of prior interactions, generation of structured output, background jobs, and validation of outputs. The architecture evolves into a more complex system.

The model is no longer the sole component; it has become just one part of a larger system. AI models are becoming orchestrators that decide which tools to use, execute actions, inspect results, and continue workflows. For instance, in a typical request-response flow, an agent could decide on a tool to use, execute an action, inspect the result, and continue the workflow.

This introduces additional layers into application architecture, with each arrow potentially representing a network request. Each component could fail, adding latency and increasing the complexity your engineering team must manage. This is why AI applications are increasingly akin to distributed systems.

A production AI application might depend on various components, including an LLM provider, vector database, relational database, object storage, search infrastructure, authentication services, external APIs, queue systems, background workers, observability platforms, content moderation services, and evaluation systems. A single user request can thus traverse multiple infrastructure boundaries, making the system distributed.

Latency becomes a significant concern in AI workflows. A single model request might take two seconds, but if an agent makes five sequential calls, including web search, database queries, and validation, the total delay could exceed several seconds. If these operations occur sequentially, delays compound, creating an important engineering challenge.

The question arises: which operations genuinely need to be sequential? Some can be performed in parallel, reducing latency and optimizing resource usage. Modern model and agent tooling focuses on orchestration and parallel decomposition, optimizing cost, latency, and unnecessary model work.