The paradigm for AI agents is evolving from single-turn, tool-using bots to more sophisticated systems that employ multi-step "chain of thought" reasoning. This enables agents to plan, reconsider, and execute complex tasks over longer durations, leading to more robust and capable applications like OpenAI's Deep Research.
OpenAI acknowledges that previous tools like the Assistants API were too complex for many developers. The new Responses API and Agents SDK are designed with a lower barrier to entry, following an "API as a ladder" philosophy that allows for simple initial use with the option to add advanced features like state management and custom tools as needed.
A core message is that current models are far more capable than most applications are leveraging. The most important skill for builders in the near term is not prompt engineering alone, but the art of orchestrating models, tools, and data, often by breaking down complex problems across multiple specialized agents.
While OpenAI provides foundational models and APIs, there is a growing need and opportunity for a third-party infrastructure ecosystem. This includes platforms for hosting virtual environments for "Computer Use" models (e.g., Browserbase) and specialized, vertical-specific infrastructure (e.g., Runloop) that provides fast, tailored solutions.
Keep pulling the thread on Nikunj Handa & Steve Coffey.