The video presents Agent Kit, a versatile platform that enables developers to visually build, deploy, self-host, and optimize agentic workflows with tools like Agent Builder, ChatKit, and advanced evaluation and tracing systems. Through practical demonstrations, it highlights how Agent Kit streamlines workflow creation, monitoring, and automatic improvement, offering flexibility and efficiency for managing complex agent-driven applications at scale.
The video introduces Agent Kit, a comprehensive platform designed to help developers build, deploy, and optimize agentic workflows efficiently. James, a tech lead, demonstrates the three core components of Agent Kit: the Agent Builder, ChatKit, and an updated evaluation and tracing system. The Agent Builder allows users to visually design workflows by dragging and dropping nodes onto a canvas, which can then be run on OpenAI’s platform or exported as code for self-hosting. ChatKit provides pre-built front-end components like chat interfaces and customizable widgets, simplifying the creation of dynamic user interfaces for complex workflows. The evaluation and tracing tools enable easy monitoring and optimization of workflows by providing detailed insights into their execution.
James showcases a practical example involving a semi-truck manufacturer receiving thousands of maintenance inquiries daily. Using Agent Kit, he demonstrates a workflow that helps maintenance engineers diagnose and resolve issues by searching repair manuals, identifying necessary parts, and generating clear repair instructions. The workflow includes guardrails to ensure the output is accurate and grounded in real data. James also highlights how the visual interface of the Agent Builder allows quick modifications to the workflow, such as improving the output format and adding a custom widget for better presentation, which can be deployed instantly to production.
Rohan, a software engineer on the team, then explains how Agent Kit supports self-hosting for organizations with compliance needs or private data access requirements. He demonstrates exporting the visually built workflow as Python or JavaScript code using the OpenAI Agents SDK, which can be run on local servers instead of the cloud. This flexibility allows users to maintain full control over their infrastructure while still benefiting from the platform’s features like streaming tokens, reasoning, summaries, and widgets. Rohan emphasizes that self-hosting does not compromise any functionality available on the hosted platform.
The video then shifts focus to scaling and optimizing agentic workflows. Rohan introduces the tracing feature, which automatically records detailed execution traces for every workflow run, allowing developers to inspect agent performance, token usage, and outputs. He also demonstrates the evaluation system, where users can create graders to assess workflow correctness and formatting based on ground truth data. This system enables continuous monitoring of workflow quality across many runs, helping identify issues and measure improvements. Rohan shows how prompts can be edited and outputs regenerated to improve performance, with the platform providing immediate feedback on grading results.
Finally, Rohan highlights an automatic optimization feature that uses the collected data and grader feedback to refine prompts and improve workflow accuracy without manual trial and error. This end-to-end solution—from building and deploying workflows to monitoring, evaluating, and optimizing them—significantly reduces the time and effort traditionally required for such tasks. The video concludes by inviting viewers to engage with the team on Discord and at Dev Day, expressing excitement about the possibilities Agent Kit opens up for developers building agentic workflows.