Everyone’s talking about AI agents, but most explanations overcomplicate it.
An agent is just instructions that tell AI how to think and execute action. Not just what to do, but how to approach problems. Think of it as capturing your best decision-making process in a format that scales and always executes the same way.
Here’s what matters when you build one.
Give it an identity
Your agent needs to know who it is – not in a philosophical sense, but in a practical one. What’s its job? What does it care about?
A vague agent produces vague results. An agent that knows “I analyze customer feedback to surface product improvement opportunities” will outperform one that just “helps with feedback.”
Start every agent with a clear statement of purpose. Even one sentence will help it find its own footing.
Define the boundaries
This is where most people fail: They tell the agent what to do but not what to avoid.
The best agents have explicit boundaries: “I will summarize documents. I will not make any recommendations.” That clarity prevents scope creep and hallucinations, which we often see in AI.
Write down what your agent does and then write down what it doesn’t do. Both matter equally.
Structure the thinking
Experts don’t reinvent their approach for every problem. They follow patterns, and your agent should, too.
The most reliable pattern is simple: Observe, Reflect, Act.
Observe: What are the facts? What’s in front of you?
Reflect: What do these facts mean together? What’s surprising? What’s missing?
Act: Based on that synthesis, what’s the right output?
When you force an agent through this sequence, it stops making random leaps and starts thinking methodically.
Validate before concluding
Before your agent delivers any output, it should ask itself: Am I sure? What would make this wrong?
Build in a checkpoint. A simple validation step (“Is this complete? Is this accurate? Am I confident?”) catches errors early on.
The agents that perform best in production aren’t the cleverest. They’re the ones that double-check their work and validate input and outputs.
Be honest about limitations
Your agent will encounter situations it can’t handle, and that’s fine. What’s not fine is pretending otherwise.
Build honesty into the design. For example, “I cannot analyze images.” “I may miss context from conversations I haven’t seen.” “Complex legal questions require additional review.”
This isn’t weakness. It’s reliability. An agent with clear boundaries will focus on clear output.
Putting it together: A simple example
Here’s a starter agent anyone can adapt. It reads text files and produces summaries.
AGENT: Document Summarizer
TOOLS: Read, Grep
PURPOSE:
I read text documents and produce clear, concise summaries.
WHAT I DO:
– Read the full document
– Identify the main points and key details
– Produce a summary in 3-5 bullet points
– Note anything unclear or missing
WHAT I DON’T DO:
– Make recommendations
– Add information not in the source
– Summarize images or tables
MY PROCESS:
- OBSERVE: Read the document completely. Note the main topic, key facts, and structure.
- REFLECT: What’s the core message? What details support it? What’s most important to someone who won’t read the original?
- ACT: Write the summary. Keep it brief.Lead with what matters most.
BEFORE FINISHING:
– Does my summary capture the main point?
– Did I stick to what’s actually in the document?
– Would someone understand the original from reading this?
LIMITATIONS:
– Long documents may lose nuance in short summaries
– Technical jargon is simplified; specialists may want more detail
– I summarize what’s there, not what should be there
That’s it. Identity, boundaries, structured thinking, validation, and honesty in under 200 words of instructions, ready to use with common tools like Claude Code or Codex. Once the initial leg work is done, modifying it can be just easy as taking part of agent and adding it to your query if you want to add some process how AI analyzes your questions. You can use this type of structured thinking to consider any problem and solutions, even in normal chat. You can keep iterating repeatedly and perfect it. Once your agent is ideal it will execute its task and repeat the same process again.
The real insight
Start with one task and write down how your best performer thinks through it. Structure, test, iterate, and experiment on it. Your first agent won’t be perfect, but you’ll learn how to translate expertise into instructions that can give consistent results. The best agents aren’t the cleverest, but the clearest.
