What I Learned Deploying AI Agents for a Real Project
Everyone's talking about AI agents. After building them into a real production workflow — including this website — here's what actually matters.
There's no shortage of AI agent demos on the internet. Slick videos showing an agent book a flight, write a report, and send a Slack message — all in one shot. What those demos don't show is what happens when the tool call fails, the model hallucinates a file path, or the context window runs out halfway through a task.
I've been building AI agents into real projects — including the EternalDev website itself, which was largely built using Claude in an agentic loop. Here's what I actually learned.
1. The hard part isn't the AI — it's the plumbing
Getting an LLM to generate reasonable output is the easy part. The hard part is everything around it: error handling, retries, state management, logging, and knowing when to hand control back to a human. Most agent failures I've seen aren't model failures — they're infrastructure failures.
2. Scope is everything
The agents that work well have a narrow, well-defined job. The ones that fail are trying to do too much in a single pass. Before you build an agent, write one sentence that describes exactly what it does. If you can't do that, the scope is too broad.
3. Human-in-the-loop isn't a failure state
A lot of teams treat human review as something to eliminate. I'd argue it's a feature. For anything with real-world consequences — sending emails, modifying data, making decisions — building in a confirmation step isn't weakness. It's the difference between a useful tool and a liability.
"The best agent I've shipped does one thing, does it reliably, and asks before it does anything irreversible. That's the bar."
4. Prompt engineering is real engineering
I used to think prompt engineering was a temporary workaround until models got smarter. I don't think that anymore. A well-structured prompt with clear instructions, examples, and defined output format consistently outperforms a vague one — regardless of model capability. Treat your prompts like code: version them, test them, and review them.
What this means for your business
If you're thinking about adding AI agents to your operations, start smaller than you think you need to. Pick one repetitive task that a person is doing manually, automate that single thing well, and measure the result. Expand from there. The teams that go big on day one almost always end up rebuilding from scratch.
- Start with one well-scoped task
- Build error handling before you build features
- Keep a human in the loop for consequential actions
- Version and test your prompts like code
- Measure before you expand
If you'd like a second pair of eyes on which task to start with, tell us what you're working on — the reply is an email within a business day, and it costs nothing.
