A strong interview strategy is no longer just about collecting common questions and rehearsing canned answers. With GPT-5.5 now optimized for complex, multi-step work and tool use, and with workspace agents in ChatGPT designed to support repeatable workflows, candidates can build a ChatGPT interview prep agent that does more than chat once and forget everything. The practical shift is simple: instead of starting over for each application, you can keep a durable prep system that turns a job posting into a structured practice plan.

That matters right now because interview prep usually gets fragmented. One chat handles the resume, another handles behavioral practice, and a third contains notes from a recruiter call. Workspace agents make it easier to keep those inputs in one place and reuse them across applications, while GPT-5.5 is a better fit for tasks that require planning, synthesis, and step-by-step follow-through. The result is not a smarter one-off prompt; it is a repeatable process you can run every week as roles, deadlines, and feedback change.

Why This Workflow Matters Right Now

GPT-5.5 is positioned for work that has multiple stages, especially when the task involves reading source material, comparing it against a goal, and then producing a structured output. That is exactly what interview preparation looks like in practice. A candidate has to read a job description, infer likely themes, map those themes to experience, and then practice answers that are specific enough to sound credible without becoming over-scripted.

Workspace agents change the workflow because they make it easier to preserve context and repeat the same process for multiple roles. Instead of treating interview prep as a fresh chat every time, you can keep a candidate-facing agent connected to the same materials, so the system remembers the job family, your weak spots, and prior feedback. That makes the output more consistent and easier to improve.

The key idea is durability. You are not trying to get one perfect answer from a single prompt. You are building a prep system that can be reused across openings, updated after each interview, and checked again before the next deadline. That is what makes the combination of GPT-5.5 and workspace agents useful this week: it supports a workflow, not just a conversation.

Collect the Inputs That Make Answers Specific

Start by gathering the materials the agent will use to reason about your candidacy. At minimum, paste the job description, your resume, target company notes, and any recent interview feedback you have received. If you have already spoken with a recruiter or hiring manager, include those notes too, because they help the agent see which skills, tools, or themes are actually being emphasized.

Then add the pieces that give the agent enough detail to personalize practice questions. Portfolio links, project summaries, and a short list of weak spots are especially useful. A weak spot list might include things like “I ramble on behavioral questions,” “I need tighter metrics in my project stories,” or “I struggle to explain architecture decisions clearly.” The goal is to give the agent enough context to create answers that sound grounded in your real work.

Keep those materials in one consistent folder or workspace so the agent can reuse them without rebuilding context every time. A stable workspace also makes it easier to update the system after each interview round. When a new question comes up, you add it to the same place, and the next prep session becomes more specific than the last.

Set Up the Interview-Prep Agent

Define the agent’s role before you ask it to practice with you. You can position it as a recruiter, a hiring manager, or a technical interviewer, depending on the round you are preparing for. That role determines the kinds of questions it asks and the standard it uses to evaluate your response.

Give the agent a simple behavioral rule: ask one question at a time and score answers against the role. That keeps the session focused and prevents the prep from turning into a crowded brainstorm. If you are preparing for a recruiter screen, the agent should emphasize clarity, motivation, and fit. If you are preparing for a technical round, it should stress correctness, structure, and explanation quality.

Use a fixed output format so each session is easy to review. For example, have it return strengths, gaps, and follow-up drills after each answer. A consistent format makes the workflow reusable because you can compare one session to the next, spot repeated weaknesses, and track whether your answers are getting sharper over time.

Run a Four-Step Prep Loop

Step 1 is to convert the job description into likely interview themes. Ask the agent to identify the recurring requirements, the seniority signals, and the competencies implied by the role. For example, a posting may point to stakeholder management, experimentation, system design, debugging, or cross-functional communication even if those exact phrases are not repeated in every bullet.

Step 2 is to generate

Sources

• Introducing GPT-5.5 (OpenAI, 2026-04-23)
• Introducing workspace agents in ChatGPT (OpenAI, 2026-04-22)
• The next evolution of the Agents SDK (OpenAI, 2026-04-15)
• ChatGPT — Release Notes (OpenAI Help Center, 2026-04-30)