Most AI projects fail because the first goal is too large.

Teams decide they want to “automate customer service” or “reimagine the sales funnel,” but those are categories, not tasks. An agent project should always begin with something small and specific. The more defined the task, the faster you can test, learn, and improve.

Think of it this way: your first agent isn’t supposed to change the business overnight. It’s supposed to prove that an agent can do one thing reliably and add value. Once that works, scaling becomes straightforward.

Let’s define what makes a good first task. A good first task has three traits: it’s self-contained, measurable, and tied to an existing workflow.

Self-contained means it can be completed within a single interaction or conversation. There should be a clear beginning and end.

Measurable means you can tell if it worked. Either the customer received the answer, the ticket was resolved, or the data was captured.

Tied to an existing workflow means it replaces or enhances something your team already does.

The worst place to start is with a task that depends on ten other systems or multiple departments signing off.

Let’s look at how Terminal Roast approaches this. Taryn, the owner, initially wants to automate everything: order-ahead, pickup coordination, product recommendations, and customer surveys. Her enthusiasm is good, but the scope is way too big. After some discussion, she decides to start with something small and clear. The agent will talk to customers about new coffee flavors, pastries, and recipes. It’ll collect those suggestions, summarize them, and send Taryn a weekly report with the top customer ideas.

And that’s it! This version is achievable, measurable, and useful. It gives Terminal Roast a way to test the technology, train their internal team, and create early value without a massive rollout.

This approach applies to any organization. Start with a single, narrow use case that aligns with your business goals.

If you run support, pick one topic or category of question that comes up often.

If you run sales, pick one step in the process where response time slows deals down.

If you run HR, focus on one employee question that gets repeated over and over.

When you start small, you can test quickly, collect data, and see whether the agent actually delivers value. If it does, expanding its responsibilities becomes a natural next step.

Here’s a good litmus test: if you can’t explain your agent’s purpose in a single sentence, its scope is too broad.

Some good examples might be:

• “Our agent helps customers check their order status.” or
• “Our agent helps new hires understand company benefits.” or
• “Our agent summarizes customer feedback weekly.”

Each of these is narrow, measurable, and connects directly to an existing workflow. This first step defines how quickly you can move from pilot to production. When the project is clear, you avoid wasted effort and guesswork later on. Most teams think the challenge is building an advanced agent.

In reality, the challenge is defining a simple one.

Terminal Roast will learn from its first small deployment, refine it, and then expand the scope once it delivers value. That same discipline applies to any organization serious about making agents part of their operations. A successful first task gives you confidence, data, and proof that the investment is working. Everything else builds on top of that.

Action: Write down one single, measurable task your agent could perform that supports an existing workflow.

If it takes more than one sentence to explain, reduce it until it does.

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