0. The starting point: start with your pain
Nobody builds an AI agent just because the technology is cool. We build one because something hurts: a problem that must be solved, a repetitive task that is exhausting, a report nobody can stand reading, or a process that breaks because it depends on one person's memory.
Before you open any tool, ask yourself three simple questions to decide whether creating this agent is worth it:
Question 1
What is bothering you?
Describe the problem in one sentence. If you cannot, it is not clear enough yet.
Question 2
Who is affected by it?
You, your team, your customers? Knowing who feels the pain sets the tone and success criteria for the agent.
Question 3
How much is it costing you today?
In time, money, or patience. This number will help you measure gains later.
Think of your agent as a "digital intern". What would it do if it could work 24/7 without getting tired? Here are examples of real problems by field that you can start solving today:
Engineering
Your pain: Ensuring projects comply with hundreds of pages of technical and safety standards.
The Agent: A Compliance Auditor that cross-checks project data against applicable standards and flags issues immediately.
Architecture
Your pain: Writing descriptive technical reports from plans and specification sheets takes hours.
The Agent: A technical writer that automatically drafts project descriptions using your firm's standard format and local requirements.
Medicine
Your pain: Reviewing long patient records before appointments consumes valuable clinical time.
The Agent: A clinical assistant that structures patient history in SOAP format, following local privacy and medical-board rules with mandatory human review.
Small Retail
Your pain: Hours lost answering the same WhatsApp questions about sizing, returns, and shipping.
The Agent: A customer support specialist that knows your inventory and store policy inside out.
Administration
Your pain: Chaos in invoice and contract management, with due dates lost in messy folders.
The Agent: A workflow organizer that automatically extracts dates and amounts into a clear schedule.
Education
Your pain: Having lots of recorded or written content but no time to create assessments and practice activities.
The Agent: An instructional designer that turns lessons into structured teaching materials.
✅ Before moving to Step 1
- You can describe the pain in one single sentence.
- You know who is affected by this pain (who will use the agent).
- You have an estimate of the current cost (time, money, patience).
Agent Vocabulary
Prompt
The instruction you give to the agent. The clearer it is, the better the answer.
Specification (Spec)
The "contract" that explains what the agent does, in plain language.
Skill (Skill)
Each thing the agent knows how to do. For example: "write emails", "review spreadsheets".
Trigger (Hook)
A rule like "whenever X happens, do Y".
Tool (Tool)
Something external the agent can use: an app, a spreadsheet, or a company system.
Markdown (.md)
A plain-text file format that both people and AI can read easily.
The path you will follow
Seven steps in sequence. Each step builds on the previous one. Do not skip stages.
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0
Start with your pain
Identify a real day-to-day problem. Without that, there is no agent.
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1
Understand your own process
Map how you solve this pain today: rules, tools, and bottlenecks.
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2
Sketch your agent workflow
List what it can do, what it uses, when it acts by itself, and the sequence of tasks.
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3
Gather study material
Research standards, manuals, and best practices. This becomes the agent's study guide.
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4
Your agent's study guide
Organize everything in one folder with text files. That is the agent's brain.
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5
Your agent's "contract"
The AI describes what it intends to build, and you approve it item by item.
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6
Time to bring the agent to life
With the contract ready, ask the AI to execute. This is where your agent is born.
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Your agent is running!
Solving the pain that was bothering you back in Step 0. Best of all: you built it yourself.
Real-world example: the small clothing store
To make this easier to picture, imagine a small clothing shop in your neighborhood. Here is how each step fits into its daily routine:
Pain
"I lose hours every day answering the same WhatsApp questions, and customers still complain that we are too slow."
Understand the process
List of the 20 most frequent questions (sizes, shipping, returns, payment methods) and the current average response time.
Sketch the flow
What it can do: check size chart, calculate shipping by ZIP code, explain return policy. What it uses: inventory spreadsheet. When it acts alone: as soon as a new WhatsApp message arrives.
Gather material
A report on customer-service best practices for fashion e-commerce plus a local consumer-protection checklist for returns.
The study guide
Folder with: return-policy.md, size-chart.md, faq.md, tone-of-voice.md.
The contract
Description: persona, friendly tone, always confirm size before shipment, hand off to a human agent when the customer asks for a discount.
Bring the agent to life
Agent in production, answering 80% of questions in seconds, with weekly transcript reviews to improve the study guide.
Understand your own process
The first step is understanding your "shop floor". You cannot teach AI to do something you cannot explain how to do manually today.
Look at your current process: where are the bottlenecks? Which rules do you follow (including unwritten ones)? Which tasks are repetitive and drain your energy for no reason? Then list the tools you already use (spreadsheets, email, specific websites).
Thinking through this and writing it down is already a great start: you will see your process in a way you may never have seen before.
Tip: investigate the problem with Deep Research
You know there is a problem, but you may not know the best practices to solve it yet. Use Gemini Deep Research now to learn the topic before planning your agent.
Prompt examples to get started:
💡 Important tip:
You need to understand the basics of the topic. If you do not know what the agent should deliver, you cannot tell whether it is working or just "hallucinating" (making things up that sound true).
✅ Before moving on
- You wrote how the process works today, even if it is still a draft.
- You read at least one Deep Research report and took notes.
- You listed the tools and systems involved in the process.
Sketch your agent workflow
With your diagnosis done, pause for a second: do not jump straight into asking AI to build the agent. First, ask it to help you design the structure of what you want to build.
In Gemini (or alternatives like ChatGPT and Claude), paste what you learned in Step 1 and ask for planning support. Planning before execution is more efficient: you can visualize the "virtual workers" and the skills each one needs, without wasting effort.
// Ask like this:
"Based on the topic I described above, help me plan a set of agents. For each one, list the required skills, tools, and triggers (hooks)."✅ Before moving on
- For each agent, define what it does, which tools it uses, and when it should start acting.
- You can explain out loud what each agent is responsible for.
Gather your agent's study material
An agent is only smart if it has high-quality material to study. This is not surface-level research: now is the time to go deep and collect real manuals, laws, and standards that govern your work.
How to use Gemini Deep Research:
Gemini Deep Research works like a private investigator: it spends a few minutes scanning the web and delivers a complete report.
- 1 Open Gemini and look for the "Deep Research" feature in the chat bar.
- 2 Define the topic based on Step 2 planning (for example: "Investigate all local safety standards for civil construction projects").
- 3 Download or copy the final report. It will become your agent's knowledge foundation.
🚨 Attention:
Read every generated report. This is not just fuel for AI. It is also a great opportunity for you to learn and stay current on the topic.
✅ Before moving on
- All researched material is saved in an easy-to-find folder.
- You read each report from start to finish and highlighted key points.
Your agent's study guide
Imagine AI as a brilliant student who has never worked at your company. The study guide is the custom learning material you hand over.
Unlike generic AI, your agent will rely on a source focused on your own data. This content defines how it behaves: it replies in your tone, follows your rules, and cites your sources. Without a study guide, answers are vague. With one, the agent behaves like an experienced team member.
What is it?
It is your agent's initial knowledge base. It defines what the agent knows and exactly how it should act.
Why Markdown (.md)?
.md files are plain text with lightweight formatting. AI tools read them well, without the visual clutter of PDF or Word files.
How to prepare the study guide:
- Create a project folder on your computer.
- Open your research reports in Google Docs and download each one as "Formatted text (.md)".
- Place those files inside the folder. Done, your study guide is ready.
✅ Before moving on
- Folder created and text files organized inside it.
- You reviewed the content and removed confidential information.
Your agent's "contract"
Now use Claude, GitHub Copilot, or alternatives like Cursor and ChatGPT (pointing to the study-guide folder created in Step 4). Ask AI to read the material and draft a contract for what will be built.
The contract is where AI explains, in plain language, exactly what it intends to do. Read it carefully and decide: "yes, this is exactly what I want" or "this rule is wrong, change it".
// Ask like this:
"Read the files in this folder and create a detailed agent specification in plain language, describing everything it should do, its rules, its tone of voice, and the situations where it must ask for human help."🚨 Attention:
Read everything. This is where you prevent AI hallucinations. A well-validated contract leads to a more reliable agent. Talk with AI or edit the text manually until it is exactly right.
✅ Before moving on
- You read the full contract and understood what is written.
- The rules and voice are yours, not the AI's.
- You defined what the agent should do when it does not know the answer.
Time to bring the agent to life
With the contract approved by you, the hardest part is done. Now give AI the order to "turn the key".
// Ask like this:
"The contract is perfect. Now execute the agent development as planned."AI will generate the files, code, and instructions needed. Your first agent is now alive and ready to solve the pain you identified at the beginning.
What you should expect as output:
- Configuration files and system instructions (system prompt).
- Folder structure with the study guide ready to use.
- Example uses and suggested test cases.
- Instructions for integration with the selected channel (WhatsApp, email, spreadsheet, etc.).
🔄 Remember:
The agent is born, but it still needs testing and tuning. Treat each run as a cycle: test -> adjust contract -> run again.
Also watch privacy: avoid adding sensitive information to the study guide (contracts, personal data, trade secrets) without confirming how your tool handles that data.
✅ Congratulations! You built your first agent!
- Your agent is live and responding.
- You already know when you will review it, and you will not postpone that.
- Compare the outcome with the original problem: was it worth it?