A Practical Guide to Validate Any Agentic AI Use Case Before You Build it

Start by spending time with the people actually doing the work – operations, support, inventory, finance, whoever’s involved.

Just try to understand: What’s the most annoying, repetitive thing they do each week that still needs judgment?

For example, in the demand forecasting agent, we realized planners were manually tweaking forecasts based on weather, local sales manager feedback, and even WhatsApp messages from distributors.

That’s where we saw the opportunity, not in the analytics dashboard, but in their workarounds.

If I can’t simulate the use case with a human doing the thinking, the agent won’t survive either.

For example, in one Voice AI experiment, we manually handled a week’s worth of support calls using predefined intents and memory-like context.

The outcome?

It taught us that half the value came not from answering queries but from asking smart follow-ups. That became the core of what we built.

A lot of ideas fail at this step. Because the agent sounds smart but doesn’t know what’s going on or can’t act on it.

To avoid that, always define:

➜ What the agent needs to decide, do, or hand over?

➜ When should it escalate to a human?

➜ What a “successful loop” looks like?

In our case, our website chatbot can observe questions from visitors, retrieve accurate answers from trained content, and even trigger calendar bookings. The simplicity of observation and action is why it works

When the use case sounds tempting, I pause and run it through this quick gut-check:

Because it will fail. Every agent hits weird edge cases or gets stuck.

So, map the failure path. What will it do when it’s unsure, fails to respond, or sees something out of distribution?

For example, with AziGPT, the fallback is to stop and route to a human (or offer help text).

This one’s underrated.

You can build the smartest AI agent, but if the users don’t trust it, it collects dust.

Before we build, I talk to the people who’ll use or be impacted by it:

➜ Do they already use something similar manually?

➜ Will they trust it to make decisions?

➜ Can they tweak it without needing a dev?

One failed project taught me that even the smartest agent dies in silence if the team avoids using it. Now, I never skip this step.

Before we hit production, I set up a feedback loop the agent can learn from:

➜ Are users overriding it? Why?

➜ Where is it asking for help too often?

➜ Is it improving week over week?

Here’s the simple feedback loop flow I use:

Don’t start with models or tools. Just sit down with someone close to the workflow – an ops manager, a customer rep, a floor supervisor.

Ask: “What slows you down every day that someone smart could help with?”

That’s often your entry point.

Absolutely. We do this all the time. I just walk through the process manually or create a simple flowchart. Sometimes, I even pretend to be the agent and answer real queries myself. If the output is useful and consistent, then I know it’s worth automating.

From what I’ve seen, it’s usually not the tech; it’s the trust gap.

People either don’t adopt the agent or quietly stop using it because they don’t know when it’ll get things wrong. You need that feedback loop and a clear failure mode from day one.

Think about it like this: the agent is a junior teammate. So, what would they need? Access to messages, customer queries, product or pricing data, workflow steps, maybe some rules.

It depends on the use case, but if you can access logs or internal tools via API, that’s often enough to start.

I treat each successful agent like a template. Once one works, I sit with other teams and ask: “Do you have a similar workflow where decisions repeat or rules are fuzzy?”

That usually unlocks a few more ideas. Reuse your validation process; it compounds.

1️⃣ AI Agent: A software program that can take in data, make decisions, and act on behalf of a person or system.

2️⃣ Validation: Before you build anything, you test the idea to see if it makes sense. This could be through manual steps, user interviews, mock flows – just enough to prove it’s useful and doable.

3️⃣ Observation Layer: Where the agent watches what’s going on – usually through APIs, logs, or platform events. It’s how the agent “knows” when to act.

4️⃣ Action Layer: Where the agent actually does something – updates a dashboard, replies to a message, triggers an automation, etc.

5️⃣ Feedback Loop: A way to monitor how well the agent is doing. If users ignore it, override it, or complain, that’s a signal to improve. Good agents learn, even if manually.