What is an AI agent? (And how to build one)

Ask five people to define "AI agent" and you'll get five different answers, and at least two of them will just be describing a chatbot. That confusion is worth clearing up, because the distinction changes what you should actually go build.

The short definition

An AI agent is software that pursues a goal by taking a sequence of actions, deciding what to do next based on what happens, using tools it's been given access to — a calendar, a CRM, an email inbox, a spreadsheet, a messaging app. The key word is action. A large language model that only replies with text isn't yet an agent; it becomes one when it can also read your data, write to your tools, and keep working without you supervising each step.

What makes something an agent, specifically

Four things, together:

1. A goal, not just a prompt. You tell it what outcome you want ("qualify this lead and add it to the CRM"), not the exact steps to take.
2. Tools. Access to the systems it needs to act — an inbox, a database, an API, a messaging channel.
3. A loop. It can look at the result of one action and decide the next one, rather than following a fixed script.
4. Persistence. Once deployed, it can run from a configured schedule, trigger, or request without requiring an active build session.

Strip out persistence and you get a very capable assistant that still needs you to hit "go" each time. Strip out tools and you get a chatbot. Strip out the loop and you get a single automated step, which is useful but isn't really "agentic" — it's closer to what tools like Zapier and Make have done for years: a fixed chain of triggers and actions someone assembled by hand.

Agent vs. chatbot vs. automation: where the lines actually are

• Chat assistant (ChatGPT, Claude, Gemini in normal use). Excellent at reasoning, drafting, and advising. Connected actions depend on the tools and permissions configured for the experience.
• Coding assistant (Claude Code and similar). Strong for hands-on software work in a coding session. CodeWords focuses on the connected automation that remains deployed after review.
• Trigger-action automation (Zapier, Make, n8n). Built around a canvas: you place the trigger, then each action, in advance. Reliable for fixed, repeatable chains, but the logic is exactly what you wired — no independent decision-making mid-run.
• AI agent. Given a goal and tools, decides its own steps, and keeps running unattended. This is the category the other three get compared against once persistence and tool-use both matter.

What people actually build with agents

The common thread across real deployments is repetitive work that involves a conversation or a decision, not just a data transfer:

• Lead capture, qualification, and routing into a CRM
• Customer support and FAQ handling across email, chat, and WhatsApp
• Research and enrichment agents that gather, summarize, and file what they find
• Scheduled reports and digests pulled from several tools at once
• Document collection, verification, and approval flows
• Booking, scheduling, and reminder agents

The full approved Jumia case study is one example of a connected, deployed agent.

How to build one, without writing code

The traditional path is to write it yourself with a framework (LangChain, CrewAI, the OpenAI or Anthropic agent SDKs) or wire it up node-by-node in a visual canvas tool. Both work, and both assume you either code or are willing to learn a canvas.

The other path, and the one CodeWords is built around, is to describe the agent you want in plain language — the way you'd explain the job to a new colleague — and have an AI builder handle the implementation:

1. Describe the agent you want, in chat: what it should do, and roughly when.
2. Cody plans it: works out the logic, and figures out which tools it needs to connect.
3. Cody deploys it. The agent goes live and runs on a schedule, on a trigger, or on demand — not just while you're watching.
4. To change it, describe the change. Cody updates the running agent; there's no canvas to rewire or ticket to file.

That collapses the distance between "I know what I want this to do" and "it's running," which matters most for the people least likely to write the logic themselves: founders, operators, and small teams who'd otherwise need to hire for it.

When an agent is the wrong tool

Not everything needs one. If a job is truly fixed — the same steps, in the same order, every single time, with no judgment calls — a simple trigger-action automation is often more predictable and cheaper to run than an agent making decisions it doesn't need to make. Agents earn their keep on work that varies: different questions, different edge cases, different follow-ups. If your process doesn't vary, you may not need an agent at all.

That doesn't mean switching platforms, though. CodeWords builds plain, fixed automations the same way it builds agents: describe the sequence you want, and Cody sets it up without the decision-making loop attached. Whether the answer to "can this be automated" turns out to be an agent or a straightforward fixed workflow, it's the same conversation with Cody either way — the platform isn't only for the agent case.

More on how CodeWords builds and runs agents: How Cody works. Start free: codewords.agemo.ai.