Build your own AI agent: a practical guide

Building an AI agent has become considerably easier than it was two years ago, and considerably easier to get wrong. The tutorials make it look like a weekend project, which it is, right up to the point where the thing has to run unsupervised against real data and real consequences.

This guide covers what an agent is actually made of, how to build one, the failure modes that show up in production rather than in demos, and the question worth asking before you start: whether the job in front of you wants an agent at all, or whether you're describing something simpler and more reliable.

What we'll cover

• What an AI agent is made of
• Before you build: is this an agent job?
• Building it, step by step
• What breaks in production
• Frequently asked questions

What an AI agent is made of

Strip away the framing and an agent has four parts.

A model that can reason. The language model decides what to do next. This is the part people focus on, and it matters least in practice, because the frontier models are all capable enough for most agent work. Your choice affects cost and latency more than whether the thing functions.

Tools it can call. Functions the agent can invoke: search something, read a record, send a message, run a calculation. An agent without tools is a chatbot. The tools are what let it act on the world, and designing them well is most of the real work.

A loop. Observe, decide, act, observe the result, decide again. The loop is what separates an agent from a single model call, and it's where the cost and the unpredictability both come from, because each pass is another decision you didn't make.

A stopping condition. When is the goal met, and what happens when it isn't? Agents that lack a firm answer here are the ones that run for forty steps, spend real money, and return something nobody asked for.

Most first agents fail on the last two. The model and the tools are the interesting parts, so they get the attention, while the loop and the stopping condition are what determine whether the thing is safe to leave running.

Before you build: is this an agent job?

Worth asking honestly, because a lot of what gets built as an agent wanted to be an automation.

The difference is who decides. An agent chooses its own steps at the moment of running. An automation does what you described, the same way every time. Neither is more advanced; they answer different questions, and we've written up the full distinction between AI agents and automations separately because it's the decision that most often gets made by default.

The quick version: if the same input producing two different results would be a problem, you want an automation. Invoice processing, compliance checks, scheduled reporting, document chasing, anything with a right answer and a regulator or a client at the end of it. Build an agent when the next step genuinely depends on what the last step turned up, which is research, investigation, and open-ended triage.

Getting this wrong is expensive in a specific way. An agent pointed at work that had a right answer will usually produce the right answer, most of the time, and then occasionally improvise, and you'll find out from the person on the receiving end.

Building it, step by step

Start with the tools, not the prompt. List every action the agent needs to take and write each as a function with a clear name, a tight input schema, and a predictable output. Vague tools produce vague agents. A tool called handle_customer invites the model to guess what that means; look_up_order(order_id) does not.

Keep the tool surface small. Five well-designed tools beat twenty overlapping ones. Every additional tool is another choice the model can get wrong, and the failure rate climbs faster than the capability does.

Write the stopping condition before the loop. Maximum steps, maximum spend, and a definition of done. Do this first, while you're thinking clearly about it, rather than after the agent has surprised you.

Give it a way to say it can't. An agent with no escape hatch will invent a path forward, because that's what it's been asked to do. An explicit "stop and hand to a person" tool is the single most useful thing you can add, and the one most often left out.

Test on the awkward cases. The happy path works almost immediately, which is misleading. Test the ambiguous request, the missing record, the API returning an error, the input in a language you didn't expect. Production is mostly awkward cases.

Watch what it actually did. Log every decision and every tool call. When an agent misbehaves you need to see the reasoning, not just the output, and reconstructing that after the fact is much harder than capturing it as you go.

Put a person on anything consequential. Money moving, messages to customers, records being deleted. A review step is what makes the rest of the automation safe to run unattended.

Memory, state, and why agents forget

The part most first builds skip, and the part that produces the strangest bugs.

A language model has no memory between calls. Everything the agent knows on step seven is whatever you passed into step seven, which means the loop has to carry its own history forward. Left to the obvious implementation, that history grows with every iteration, and by step fifteen you're sending a very long context, paying for it each time, and watching the model lose track of the original instruction somewhere in the middle.

There are three things worth deciding early.

What the agent carries between steps. Usually a summary of what's happened and the results that still matter, rather than the full transcript. Deciding what to keep is a design choice, and the default of keeping everything is the expensive one.

What survives between runs. If the agent handles a customer on Monday and the same customer returns on Thursday, does it know? That's storage you have to build, not something the model provides, and whether you need it at all is worth asking before you build it.

What it must not remember. Personal data carried between runs or between customers is a privacy problem you've created. Be deliberate about what gets written down and how long it stays.

None of this is difficult, and all of it is easier to decide now than to retrofit after the agent is running and the context bills are arriving.

What breaks in production

The demo and the deployment fail differently, and the deployment failures are duller.

Cost drift. An agent that averages six model calls in testing averages nineteen against messy real inputs, because messy inputs mean more loops. Cap the steps and the spend.

Silent wrongness. The agent returns something plausible and incorrect, and nothing errors. This is the failure mode with no alarm attached, and it's why the review step matters more than the monitoring.

Tool errors handled badly. An API times out, and instead of stopping, the agent decides to try something else. That improvisation is the whole point of an agent, and it's also how an agent ends up doing something nobody sanctioned.

Prompt drift. You fix one behavior by adjusting the instructions, which quietly changes another. Agents have no clean way to make a targeted change, which is the maintenance cost people underestimate.

It didn't need to be an agent. The most common one. Six months in, the loop has been constrained so heavily with rules and guard rails that it now follows a fixed path with extra steps and a model bill attached.

That last failure is worth taking seriously, because it's the pattern rather than the exception. Teams reach for an agent, discover that unpredictability is unwelcome in their process, and spend months constraining the agent back into an automation.

If what you actually want is defined work that runs reliably without you, CodeWords is built for that directly. You describe the job in plain language, and Cody, the automation builder, builds it, connects it to the tools you already use, and deploys it. When the process changes you describe the change, which removes the maintenance problem that makes people reach for adaptive systems in the first place.

Automations connect to more than 3,000 integrations. The free plan covers light use, with Pro at $39 per month and Business at $100 per month as usage grows. Current details are on the pricing page.

Frequently asked questions

Do I need to know how to code to build an AI agent?

For a genuine agent, some. Frameworks have lowered the bar a long way, but you'll be writing tool definitions, handling errors, and debugging a loop, and those are programming tasks whatever the marketing says. For an automation, no: you describe the process in plain language, and the expertise that matters is knowing how your own work runs.

Which framework should I use?

Less important than the questions above. The common choices handle the loop, the tool calling, and the state management competently, and your agent will succeed or fail on tool design and the stopping condition rather than on the framework. Pick one your team can debug.

How much does running an agent cost?

It varies more than people expect, because cost scales with loop iterations rather than requests. A task that takes four model calls on a clean input can take twenty on a messy one. Measure against real inputs rather than test cases, and set a spend cap before you deploy rather than after the first surprising invoice.

How do I stop an agent doing something I didn't intend?

Constrain what it can reach rather than instructing it not to misbehave. Tools it doesn't have are actions it can't take, and that's a much stronger guarantee than a line in a prompt. Add an explicit escalation path, cap the steps, and keep a person on anything irreversible.

Can an agent and an automation work together?

Yes, and it's often the right shape. An agent handles the genuinely open-ended part, such as investigating or drafting, and hands to an automation that routes, logs, and delivers on a fixed path. You get adaptability where it helps and predictability where it matters.

How long does it take to build a first agent?

A working prototype takes an afternoon. Getting it reliable enough to run unattended against real data takes considerably longer, and that gap is where most agent projects quietly stop. Budget for the second part, because the first part is the one the tutorials cover.

What's the most common mistake?

Building an agent for work that had a right answer. The second most common is giving it too many tools. Both come from the same instinct, which is to make the thing capable before making it predictable.

Should the agent run on a schedule or on demand?

Both are normal, and the choice affects the design more than people expect. An agent triggered by a person is supervised by definition, so a looser stopping condition is survivable. An agent running on a schedule at three in the morning has nobody watching, which means the caps, the escalation path, and the logging all have to be right before you turn it on rather than after.

How do I know the agent is still working?

Decide what "working" looks like as a number and watch that, rather than watching for errors. Agents fail silently far more often than they crash, so an error rate of zero tells you very little. Count the outcomes you actually want, whether that's tickets resolved or records enriched, and alert on the count dropping rather than on exceptions appearing.

Related reading

• AI agents vs automation: what's the difference?
• What is an AI agent?
• AI agents builder: how to pick the right one
• CodeWords integrations and templates

Build the agent if the work genuinely needs one. If what you're describing is a process you could explain to a new colleague, an automation will serve you better.