I’ll admit it. The first time I heard the word “agent,” I pictured a chatbot with a few extra buttons. I had it completely wrong. Then I found a post from an AI professional who explains this so clearly that I wanted to pass it straight to you. The author’s main point is simple: most people building their first AI agent skip straight to the tools. That’s the mistake.
The post doesn’t just tell you what to avoid. It gives you a clear sequence to follow, so let’s go through it piece by piece.
What an agent actually is
The post’s author starts with a definition I found really useful. An agent isn’t a chatbot with extra steps. It’s a system that takes a goal, chooses an action, uses a tool, checks the result, and keeps going until the job is actually done.
That loop is the whole point, and most builders miss it entirely. They wire up an API call, call it an agent, and then wonder why it breaks the moment something unexpected happens. A single API call can’t recover from surprises. A loop can, because it checks its own work and adjusts.
I also liked how honest the creator is. They admit making this exact mistake early on. They thought they understood automation, but they didn’t understand agents, and they had to relearn it all from scratch. Hearing that from someone who’s been through it makes the advice stick.
The 6 building blocks
The expert explains that founders who get this early stop seeing an agent as one clever prompt. They see it as six building blocks working together. Here’s the breakdown the author wishes someone had handed them on day one:
- Model. The reasoning engine making the decisions.
- Instructions. The goal, the rules, the boundaries.
- Context. The relevant information for this exact task.
- Tools. Search, APIs, files, email, code.
- Memory. What it stores and carries across steps.
- Guardrails. Permissions and approvals for risky moves.
Miss any one of these and the agent either does too little or too much. That line is worth remembering. Without good instructions or context, the agent stalls or wanders off. Without guardrails, it might send an email or delete a file you never meant to touch. Each block covers a gap the others leave open.
How to build your first agent, step by step
Next comes the practical part. The creator lays out the exact sequence they’d use to build a first agent. I’ve added a short note on why each step matters, so the reasoning is easy to follow.
- Pick one narrow job. Not five. One. A narrow job gives the agent fewer ways to fail and makes problems much easier to spot.
- Define success. Inputs, outputs, limits, done criteria. If you can’t say what “done” looks like, the agent can’t either, and it will keep running or quit too early.
- Write instructions. What to do, which tools, when to stop. The stopping rule is the one people forget, and it’s what keeps the loop from spinning forever.
- Connect only the data it needs. Nothing extra. Extra data adds noise, cost, and risk without making the agent smarter.
- Add 1 to 3 tools. Only ones it genuinely needs. Every extra tool is another decision the model has to make, which means another chance to pick the wrong one.
- Build the loop. Decide, act, observe, repeat. This is what turns a script into an agent, because the “observe” step lets it catch and fix its own mistakes.
- Add guardrails before anything consequential fires. Approvals for risky actions like sending, paying, or deleting protect you while you’re still learning how the agent behaves.
- Test it against normal tasks, edge cases, and bad inputs. The happy path tells you almost nothing. The weird inputs show you where it breaks.
- Deploy gradually. Recommendations first, approvals next, automation last. Trust gets earned in stages, and every stage gives you real data before you hand over more control.
The author didn’t just come up with this list in theory. They tested this exact sequence with a small team last quarter, and the result surprised me.
The version that worked wasn’t the one with the most tools. It was the one with the tightest loop and the clearest guardrails.
That runs against what a lot of builders assume. It’s tempting to think more connections mean a more capable agent. In practice, focus beat features.
A simple example that makes it click
To make this concrete, the original poster shares the example that made it all click for them:
- Goal: research a company, produce a competitive brief.
- Tools: web search and file search. That’s it.
The agent receives the company, researches sources, collects evidence, analyzes findings, verifies the key claims, then writes the brief. No extra moving parts. Just a tight, repeatable loop.
Look at the “verifies the key claims” step. That’s the observe part of the loop doing its job. Instead of trusting its first pass, the agent checks its work before writing anything. You could use the same pattern for vendor comparisons, candidate research, or prepping for a sales call. Same two tools, same loop, different goal.
The whole thing in one line
If you want the formula in a single sentence, the creator sums it up like this:
Model plus Instructions plus Tools plus Context plus Memory plus Loop plus Guardrails plus Evaluations. That’s your agent.
Notice that two extra pieces show up next to the six building blocks: the loop and evaluations. The building blocks are the parts, the loop is what keeps it moving, and evaluations tell you whether it’s actually working. Leave those out and you just have a pile of parts.
Before you start building
The author’s closing advice is short and solid. If you’re building your first agent this year, keep it narrow, keep the loop tight, and add scope later.
I think this matters right now because agent tools are everywhere, and it’s easy to connect ten integrations in an afternoon. That speed is tempting, but it’s exactly how people end up with agents that break on the first odd input. Starting small is the faster route in the long run.
The post also has a good suggestion for teams. If someone on your team is about to overengineer their first agent, send them this before they start. It could save them weeks of frustration.
The author ends with a question I’d love for you to think about too: what would you automate first if you built one agent this month? Pick one narrow job, write down what “done” means, and go from there.
For the full breakdown in the creator’s own words, check out the original LinkedIn post and join the conversation in the comments.