Building an Agentic Workflow: Orchestrating a Multi-Step Software Engineering Interview

As language models advance, the agentic workflow emerges as a method to create intelligent systems where AI agents autonomously execute complex tasks by interacting with external tools and APIs, despite challenges in real-world execution. Orchestration plays a crucial role by integrating large language models (LLMs) with robust workflow engines to manage tasks and maintain reliability across multiple steps. An example is an automated software engineering technical interview application, which uses tools like OpenAI for reasoning, Google Docs for formatting, and SendGrid for communication, all orchestrated by Orkes Conductor. This application mimics a human-led coding interview, involving candidate intake, question generation, response evaluation, and report delivery, highlighting the potential of agentic workflows for production-level deployment. This system is built using a modern tech stack, including Next.js for the frontend and Python for the backend, and can be customized or extended for various interview types or integrated with HR systems for enhanced functionality.