In our previous post, we explored how traditional surveys fail to capture the depth businesses need for meaningful insights. The choice has always been binary: deep qualitative research with 20-30 participants, or shallow quantitative surveys with thousands.

AI interviews are changing this equation, enabling businesses to conduct in-depth, conversational research with hundreds or thousands of participants simultaneously.

Online surveys have dominated market research for decades. They're fast, cost-effective, and can reach thousands of respondents. But anyone who has analyzed survey data knows the frustration: results often lack depth and fail to capture the "why" behind consumer behavior.

The truth is, while surveys excel at collecting quantitative data, they struggle to provide the rich, contextual insights that drive real business decisions.

We noticed something remarkable while working with Claude in our terminal. Large Language Models are fundamentally passive—they respond to prompts, provide answers, then wait dormant until the next human input. This has been the defining characteristic of every AI interaction since the beginning.

But there's an emergent capability that changes everything. AI agents can now execute sleep commands and schedule their own activity. This transforms them from passive responders into active participants that can monitor, wait, and return with updates autonomously.

AI coding is evolving fast. With Claude Code support for custom commands, it's time to upgrade your workflows. One of the most powerful advanced agentic coding techniques is parallel development with Git worktrees—running multiple Claude agents simultaneously on different branches of your codebase using custom slash commands. Our adoption of this technique is inspired by the benchy repository from this video.

Let's break it down step-by-step so you can replicate this advanced workflow in your own repo using custom Claude Code commands.

MCPs work like magic. Internally we use them relentlessly inside Cursor, for Linear issues in particular. We decided to ship an MCP server with Agent Interviews mainly because it made sense for us to have it on our own product for testing, before we even provided it to our customers. We built it quickly and made choices-of-least-resistance so there may be better ways to do everything. This is why we wanted to share our experience, would love to hear your feedback.

So the headline is we decided to implement an MCP server, Model Context Protocol (MCP), in our Django application, built on top of our existing API endpoints, and get it working with Cursor and Claude 3.7.

Ready to harness the power of AI for gathering feedback or insights? Agent Interviews makes it easy to create and deploy AI agents that conduct interviews and generate insights in real time. This guide walks you through the essential steps to get your first interviewer up and running quickly.

Goal: Launch a basic AI interviewer focused on your topics and insights, and start collecting feedback instantly.

Conversational AI platforms like Agent Interviews aren't just a technical novelty; they offer practical solutions for gathering insights across a wide range of business functions. By enabling scalable, consistent, and in-depth conversations, AI interviewers open up possibilities beyond traditional methods.

Let's look at some compelling use cases:

You've successfully deployed your AI interviewer using Agent Interviews, and the conversations are rolling in. Now comes the crucial part: turning those raw conversations into meaningful insights. Unlike survey data, AI interview results offer rich, qualitative context – but how do you effectively analyze it?

For years, surveys and questionnaires have been staples in the user researcher's toolkit. They offer scale and quantifiable data, but how much do they truly reveal about user experiences, motivations, and pain points? Often, the richest insights lie hidden in the nuances of conversation – something traditional surveys struggle to capture.

What if you could have probing, dynamic conversations with your users, at scale?

So you've created the perfect AI interviewer in Agent Interviews using a well-crafted template. Now, you need to share it with your target audience. The primary mechanism for this is the Shareable Link – a unique URL tied to your specific interviewer instance. While easy to generate, it's worth considering best practices for managing these links securely and effectively.

Adopting a new tool like Agent Interviews for AI interviews doesn't mean abandoning your established processes. In fact, its value is often maximized when integrated thoughtfully into your existing user research, product development, HR, or customer feedback workflows.

How can AI interviews complement, rather than replace, what you already do?

There's a lot of buzz around AI generating synthetic data – creating artificial datasets to train models or simulate scenarios. While valuable in some contexts, this focus can overshadow a perhaps more profound application of AI: its ability to help us connect more deeply and at scale with real human beings.

Instead of just simulating users, what if AI could help us listen better to the ones we actually have? Platforms like Agent Interviews represent this shift, using AI not to replace human interaction, but to facilitate and scale authentic conversations.

Imagine trying to conduct dozens or hundreds of user interviews manually. Maintaining consistency in questioning, tone, and focus across all those sessions would be a significant challenge, introducing potential bias and making comparisons difficult. This is where the concept of Interviewer Templates in platforms like Agent Interviews becomes incredibly valuable.

For decades, gathering rich qualitative data meant resource-intensive one-on-one interviews or focus groups. While incredibly valuable, these methods inherently limit scale. Now, advancements in conversational AI are ushering in a new era for qualitative research, enabling deeper understanding at unprecedented speed and scale. Platforms like Agent Interviews are at the forefront of this transformation.

What trends are shaping the future of AI-powered interviews?

Creating a compelling AI interviewer persona goes beyond writing good prompts—it's about strategically structuring your interview stages to create a coherent, purposeful personality that guides conversations naturally. Agent Interviews' Stage Builder gives you the tools to craft nuanced personas through structured stages, targeted topics, strategic insights, and contextual reference documents.