Announcing AgentKit, fresh Evals, and RFT for agents
OpenAI releases three key resources to help developers move from prototype to production faster: AgentKit, a unified toolkit for building, testing, and shipping agentic applications; expanded Evals features for measuring agent performance reliably; and Reinforcement Fine-Tuning (RFT) that lets agents improve at real tasks over time. Together, these tools aim to shorten the distance between an idea and a production-ready agent.
OpenAI Launches AgentKit, Expanded Evals, and Reinforcement Fine-Tuning for Agents
Today we are rolling out a fresh set of resources aimed at helping developers move their projects from early prototype to production more quickly. The release includes three main pieces: AgentKit, a broader set of evals features, and reinforcement fine-tuning designed specifically for agents.
AgentKit gives builders a more complete toolkit for creating, testing, and shipping agentic applications. Instead of stitching together separate components on their own, developers can use this unified kit to speed up the whole workflow, from the first draft of an agent to a version that is ready for real users.
The expanded evals capabilities make it easier to measure how well agents actually perform. Reliable evaluation is one of the hardest parts of moving beyond a demo, and these improvements are meant to help teams test their agents more thoroughly before releasing them to the public.
Finally, reinforcement fine-tuning for agents lets developers improve their agents through reinforcement learning, so the agents get better at completing real tasks over time rather than simply following static instructions.
Together, these tools are built with one goal in mind: shortening the distance between an idea and a production-ready agent.
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