Lately, there’s been talk of AI agents everywhere. Every company has their roadmap full of “agents that will revolutionize this and that,” but when you scratch a little, you realize few have actually managed to build something useful that works in production.
Recently I read a very interesting article by LangChain about how to build agents in a practical way, and it seems to me a very sensible approach I wanted to share with you. I’ve adapted it with my own reflections after having banged my head more than once trying to implement “intelligent” systems that weren’t really that intelligent.
The problem with agents
First of all, let me tell you something I’ve learned the hard way: not everything needs an agent. Yes, you read that right. Sometimes the most elegant solution is a simple bash script or a function that does exactly what you need.
Agents are slow, expensive, and, let’s be honest, sometimes unpredictable. If you can solve your problem with traditional code, do it. Reserve agents for tasks that really require that “something extra” of intelligence and adaptability.
A practical framework
After several projects (some successful, others… well, others better not mentioned), I’ve come to the conclusion that you need a structured process. Here are the 6 steps that really work:
Step 1: Define the job with concrete examples
This is fundamental. Before writing a single line of code, sit down with pen and paper (you know I’m one of those who thinks better on paper) and define exactly what your agent should do.
The golden rule: if you couldn’t explain it to a smart intern, then it’s not well defined.
Create 5 to 10 concrete examples of what you expect it to do. For example, if you want an email management agent:
- “Email from Jeff Bezos asking for a meeting next week” → High priority, check calendar, propose times
- “Automated marketing newsletter” → Ignore or move to specific folder
- “Client asking about prices” → Search documentation, prepare response based on updated info
If you can’t create concrete examples, your idea is poorly focused. Period.
Step 2: Design the operating procedure
Here comes the part that many find tedious but is crucial: write step by step how a human would do the job. Don’t skip this step, trust me.
Following the email agent example:
- Read email and analyze sender
- Query contact database for context
- Classify by urgency and intent
- If it’s a meeting: check calendar and propose times
- Draft appropriate response
- Review and send (with human supervision)
This exercise will reveal many things you hadn’t considered and save you a lot of time later.
Step 3: Build an MVP with prompts
This is where many teams start to complicate their lives. Don’t try to do everything at once.
Identify which is the most critical reasoning task (usually classification or decision making) and create a prompt that does it well. Just that.
In our email example, we’d start only by classifying emails by urgency and intent. Nothing more. With hand-entered data:
Email: "Can we meet next week to discuss the roadmap?"
Sender: "Amazon CEO"
Output: Intent="Meeting", Urgency="High"
When this works well with your step 1 examples, then move forward. Not before.
Step 4: Connect and orchestrate
Now yes, it’s time to connect your prompt with real data. APIs, databases, whatever you need.
But be careful here: don’t complicate yourself with super sophisticated architectures. Start simple:
- A webhook that triggers with new emails
- Query the Gmail API
- Enrich sender data
- Pass through your tested prompt
- Structured response
Complex orchestration can come later, when you have confidence that the basic logic works.
Step 5: Test and iterate
This is where the wheat is separated from the chaff. You have to test systematically:
Manual testing first: use your step 1 examples. If these basic cases don’t pass, stop and fix before continuing.
Automated testing after: when manual works, automate the tests. Define clear success metrics. No “it seems to work well” nonsense.
For the email agent:
- Professional and respectful tone ✓
- Correct intent detection ✓
- Efficient tool use ✓
- Response quality ✓
Step 6: Deploy, scale, and refine
Here begins the good (and complicated) part. Deploy with real users but with active human supervision.
Monitor everything: costs, latency, accuracy, edge cases. Real users will use your agent in ways you never imagined.
And something important: launch is the beginning of iteration, not the end of development.
Reflections from the trenches
After implementing several systems of this type, let me tell you some things I’ve learned:
Agents aren’t magical. They need clean data, well-structured context, and clear logic. If your manual process is chaos, the agent won’t fix it.
Human supervision is key, at least at the beginning. I’ve never seen an agent that works perfectly from day one without human intervention.
Cost can skyrocket quickly. Keep very tight control of how many calls you make to the LLM and optimize from the start.
Users always find creative ways to break your system. Prepare yourself mentally for that.
Conclusion
Building useful agents isn’t impossible, but it requires discipline and a methodological approach. Don’t get caught up in the hype and apply the same rigor you would to any other software project.
Start small, stay focused on real use cases, and don’t be afraid to iterate. The best agents I’ve seen have been built step by step, with lots of patience and real testing.
And remember: sometimes the smartest solution is to not use AI at all. Your 50-line script that works perfectly is worth more than the most sophisticated agent in the world that fails 20% of the time.
Have you tried building any agent? I’d love to know your experience.