Keeping AI product development workflows in check

It's a great time to be building AI products. Teams are shipping capabilities that weren’t possible even a couple of years ago, and are beginning to think even more ambitiously. But with that momentum (and everything that makes AI challenging to work with) product development workflows can get messy fast.

For example, it’s easy for agents to fail to enforce user-specified filters or misinterpret complaints, producing at best incomplete and at worst irrelevant results. A trip-planning agent that thinks you’re looking for travel tips for Paris, TX, not Paris, France. Or an agent that repeatedly misunderstands a customer’s question, so it fails to route them to the proper help channel.

Luckily, there are ways to evade the chaos. In this article, we’ll share some of the key tenets to keep in mind when building and scaling your AI product development workflows, and how a team product analytics platform like DBNL can streamline your process and help keep things in check.

The AI product flywheel

To start, let’s review the simplified AI product flywheel. Every AI product team should be following these steps, more or less, to ensure that they’re accurately capturing the data they need to continually improve their product. There are four stages: deploy, analyze, improve, and repeat.

Deploy: Ship your product to real users, to get real data. Equip your product with the proper tooling for analysis and experimentation, so that you’re not shipping blind.

Analyze: Uncover issues in production, and gain insight into what might be causing those issues.

Improve: Take issues offline, engineer, and optimize them before returning changes to production.

Repeat: Go to Deploy

AI product tenets

As you build and launch your flywheel, there are several tenets to keep in mind to get the most out of it—particularly during the analyze phase.

Success is proportionate to experimental throughput

The more frequently you deploy and iterate, the faster you can adapt and iterate—and the faster your product improves. Teams that aren’t experimenting continuously fall behind.

The human in the loop still matters

Multi-turn conversation, or chat, has become the default interface for the most valuable agent products. You need insight into intent—what your users are doing impacts how your AI product performs, and vice versa. 

Analyze for quality both online and offline

Offline evals are useful, but real usage is the true measure of your product’s value and quality. Every accept, reject, and clarification a real user makes is an extremely useful feedback signal. Teams that capture and reason about those signals get a much clearer picture of where their product actually stands.

Transform traces into insights

AI product analytics takes a lot of work. Turning production data into actionable improvements means grouping issues, quantifying their impact, and tracking whether fixes hold over time. That work adds up, which is where Distributional comes in.

DBNL gives AI product teams analytics built for production agents. It summarizes KPIs at the session, trace, and span level, and enables you to slice data into production experiment variants so you can compare variants across quality, cost, and speed signals. DBNL also surfaces grouped insights from production issues, so you can then take action quickly. The goal is to help you reason clearly about what to fix next, so your team can stay focused on building your next ambitious project.

Get started

The easiest way to get started is to use a free SaaS demo account to review this example and other examples that we’ve pre-loaded in Distributional. Next, you can install our sandbox locally on your laptop in ten minutes and run through a tutorial that shows you how to use Distributional for a toy example. Once more familiar with our functionality, you can install the full service for free using a Terraform Module or Helm Chart. We are happy to help through any step of this process, so reach out at support@distributional.com with any questions.

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