Building Your First AI Agent? Read This Before You Spend a Dollar.
Everyone wants an AI agent.
At least that's what it feels like over the last few months.
Almost every conversation eventually gets there.
"We're thinking about building an AI agent."
Sometimes it's for customer support. Sometimes it's for internal knowledge. Sometimes it's for operations.
The use case changes. The first question usually doesn't.
What surprises me is that very few companies start by talking about the work they want the agent to perform.
Instead, they start by asking about the technology.
- Should we use OpenAI?
- Claude?
- Gemini?
- Should we fine-tune?
- Do we need MCP?
- What about RAG?
Those are good questions. They're just not the first questions.
Over the last year, one thing has become pretty clear to me.
Most AI projects don't struggle because the models aren't good enough. They struggle because the business wasn't ready for them.
If you're considering building your first AI agent, here are a few things I'd think about before writing a single line of code.
Don't automate a vague idea.
This is probably the biggest one.
"We want an AI agent for customer support."
Okay. What exactly should it do?
- Answer FAQs?
- Issue refunds?
- Update the CRM?
- Escalate complaints?
- Schedule appointments?
The clearer the task, the easier everything else becomes.
The companies that get the most value from AI usually start with one repetitive workflow rather than trying to automate an entire department.
One client conversation still sticks with me.
They wanted an AI assistant because everyone else seemed to be building one.
After about twenty minutes of discussion, we realised the real issue had nothing to do with AI.
Their employees spent nearly two hours every day searching for documents across email, SharePoint and an ERP system.
They didn't need a chatbot. They needed a faster way to find information.
The solution changed completely once the actual problem became clear.
Think about your data before thinking about your model.
AI isn't magic.
If the information is outdated, duplicated or scattered across five different systems, the agent will struggle no matter which model you choose.
This isn't the exciting part of AI. It's also the part that determines whether the project succeeds.
Expect your first version to be wrong.
This is something software teams understand instinctively. Business teams sometimes don't.
The first version of an AI agent is usually where you discover edge cases you never considered.
Customers ask unexpected questions. Documents contain inconsistent information. Internal processes have exceptions that nobody documented.
That's normal. The goal of version one isn't perfection. It's learning.
Don't remove humans too early.
One of the biggest misconceptions around AI agents is that they should immediately replace people.
In most business environments, they work best when they remove repetitive work while leaving judgement to humans.
- Reviewing contracts.
- Approving payments.
- Handling complaints.
Those decisions still benefit from human oversight. At least today.
Measure something that matters.
One question I like asking is surprisingly simple.
"What number should improve if this project succeeds?"
If nobody can answer that, it's worth pausing.
- Maybe it's response time.
- Maybe it's support cost.
- Maybe it's onboarding speed.
- Maybe it's employee productivity.
Without a measurable outcome, it's difficult to know whether the AI created value or simply generated interesting demos.
One final thought.
The companies that benefit most from AI over the next few years won't necessarily be the ones experimenting with every new model that gets released.
They'll be the ones that deeply understand their own business processes.
Because once you understand the workflow, choosing the technology becomes much easier.
It's usually the other way around that causes problems.