Agent Interviews Team

Most “MCP integrations” ship as thin wrappers over existing REST endpoints because it’s the fastest way to get something working, but that convenience often fights how agents actually reason and act. If you want MCP that agents can use reliably, design it like an action‑oriented RPC interface rather than a collection of CRUD‑style endpoints.

Bots are contaminating survey data at an alarming rate. Automated scripts, server farms, and sophisticated AI responses can completely distort your findings. The good news is that most bot attacks follow predictable patterns, and you can stop them with layered defenses that don't hurt real participants.

Beyond automated bots, survey fraud often comes from humans gaming the system. People create fake demographics to qualify for studies, submit multiple responses for extra rewards, or provide low-effort answers just to collect payment. These behaviors can be harder to detect than bot activity, but the right combination of verification, incentive design, and quality checks can dramatically reduce fraud while maintaining genuine participation.

When you're running research at scale, quiet moments add up. A muted mic, a doorbell, a thinking pause—each can stall momentum, chip away at completion rates, and flatten the quality of insights. Our approach is simple: keep the conversation flowing, stay unfailingly polite, and protect the dignity of every participant.

Voice gives research the human texture surveys often miss—but only if the data is genuine. Our goal is simple: make sure the person speaking is real, engaged, and part of the intended audience. To do that, we run a light‑touch AI quality check every two minutes, stream results to a live dashboard, and deliver a final evaluation at the end of each interview.

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-powered qualitative research studies are changing this equation, enabling businesses to conduct in-depth, conversational research with hundreds or thousands of participants simultaneously through comprehensive market research.

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.

Once your AI research agents have conducted qualitative research interviews and conversations with participants, you'll have a wealth of research data waiting to be analyzed. The real value lies not just in collecting this qualitative research data, but in extracting actionable insights that can drive business decisions, improve products, or inform research strategies.

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

Goal: Launch a basic AI research agent focused on your research topics and insights, and start collecting qualitative research data instantly.

AI-powered research 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 qualitative research conversations, AI research agents open up possibilities beyond traditional research methods.

Let's look at some compelling use cases:

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 conduct probing, dynamic qualitative research studies 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-powered qualitative research 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-powered qualitative research studies 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.

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