It would seem that curl + jq represents most of the solution these days. I recently learned that jq programs can be used in the other direction to effectively patch a json document too.
Putting both of these things together, I am running out of problems to solve as a human developer in the space of AI agent integration.
Assuming all your systems speak JSON right now, you are already 100% there with frontier models. You don't need to write a single line of code. Even if your json documents are monstrous in size. Put a size limit around the jq output and the agent will iterate with various filters until it finds a concise view of the problem. None of this behavior needs to be prompted or orchestrated anymore. Give the agent the tools and set reasoning level > 0.
Putting both of these things together, I am running out of problems to solve as a human developer in the space of AI agent integration.
Assuming all your systems speak JSON right now, you are already 100% there with frontier models. You don't need to write a single line of code. Even if your json documents are monstrous in size. Put a size limit around the jq output and the agent will iterate with various filters until it finds a concise view of the problem. None of this behavior needs to be prompted or orchestrated anymore. Give the agent the tools and set reasoning level > 0.