Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate work to other AI agents. At that point, you are no longer building a simple application with an AI feature.
Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate...
OpenAI's agent guidance similarly describes systems built around models, tools, orchestration, guardrails, and potentially multiple agents.
At that point, you are no longer building a simple application with an AI feature.
The page is ready to read now. The fuller skim-friendly version will appear here automatically.
Modern AI applications increasingly retrieve information, call external APIs, execute tools, interact with databases, invoke multiple models, run background tasks, maintain state, and sometimes delegate work to other AI agents. At that point, you are no longer building a simple application with an AI feature. This shift is one of the most important architectural changes happening in software engineering today.
There are already challenges around latency, cost, authentication, rate limits, and error handling, but the architecture remains relatively easy to reason about.
AI applications used to be relatively simple. Now imagine adding a few real-world capabilities.
Open the app view to save this story, compare related coverage, and continue from the same source.