Over the past six months, I’ve been deep in the trenches working with AI agents. I’ve built prototypes, tested frameworks, broken things, and occasionally gotten something to work the way I intended.

It’s been exciting — but also humbling.

There’s a lot of hype out there, and while much of it is grounded in genuine progress, I’ve seen first-hand how different the day-to-day reality is from the glossy demos and blog posts.

If you’re getting started with AI agents or just curious about what it’s really like, here are eight honest takeaways from my experience.

1. Most “AI Agents” Aren’t Really Agents

You’ve probably seen demos of AI agents that can schedule appointments, handle customer support, or generate content for your brand. They look polished. They seem smart. But here’s the truth:

Most of these are just well-structured workflows with a bit of LLM magic sprinkled in.

They aren’t making real decisions. They’re running predefined steps — like “if the message includes a time, call the calendar API” — and using the LLM only to interpret input or format output. That’s automation, not agency.

If you’re expecting autonomy, judgment, or adaptation, you’ll be disappointed — unless you build it yourself.

2. We’re Early. Really Early.

There’s a shared moment a lot of people have when they first dive into AI: “This is going to replace us.”

And maybe it will, someday. But not today.

As developers, we already know where AI can help right now — drafting code, writing tests, summarizing input. But there are still major limitations in reasoning, context retention, memory, and reliability.

AI agents aren’t anywhere close to replacing full-time employees in most domains. There’s a long road ahead.

3. The Happy Path Isn’t Enough
Almost every tutorial or demo out there shows the same types of agents: the travel assistant, the research bot, the meeting note taker.

They’re helpful — but they only show the “happy path.” The scenario where everything works, every tool returns a valid result, and the user asks perfect questions.

Building real agents means dealing with broken APIs, vague input, long-running tasks, and dead ends. The real world is messy — your agents need to be prepared for that.

4. Agents Are Slow (and That’s a Problem)

One of the biggest performance surprises when building agents is how slow they can be.

You’re likely calling an LLM multiple times, invoking tools or APIs, chaining results, and possibly routing through sub-agents. Each step adds latency.

Unlike ChatGPT — where billions have been spent on speed and optimization — your custom agent lives in your infra. And speed will matter to your users.

If you want the experience to feel responsive, you’ll need to optimize around latency: infrastructure, parallelism, UI tricks like optimistic rendering — whatever you can do to mask the delay.

5. Tooling Is Easy. Prompting Is Hard.

Writing the code to call a tool? Easy.

Getting your agent to consistently decide when to call that tool — and how to use it — based on messy, real-world inputs? Not so easy.

Prompts are where most of the real work happens. They’re fragile. Small wording changes can completely shift the model’s behavior. And you’ll spend a lot of time iterating, debugging, and rephrasing.

It can be frustrating. But when you finally get a clean, natural response chain — it’s worth it.

6. Modularity Comes at a Cost

As engineers, we’re trained to decompose problems into smaller units — functions, services, components.

It’s tempting to do the same with agents: one for scheduling, one for research, one for recommendations, and a router that directs traffic.

That works, but it introduces two big problems:
• Context fragmentation: It becomes harder to maintain state across multiple agents.
• Latency overhead: Every routing decision is another round-trip with the model or server.

Sometimes, fewer agents with more tightly scoped memory works better than trying to modularize everything too early.

7. Prompting > Coding (and That’s the Job)
If you want to get good at building agents, you need to get good at writing prompts.

Use Gemini, ChatGPT, or your LLM of choice to help generate and improve them. But don’t blindly trust the output. Always read it. Test it. Make sure the behavior aligns with what you intended.

Too many devs treat LLMs like a black box that “just works.” That leads to brittle systems. If you’re not reading and refining your prompts, you’re not really building.

8. Frameworks Matter, but Start Simple

My first experience building agents was with LangGraph. It’s powerful and flexible — but also low-level and hard to grasp at first. The documentation isn’t always clear, and there are often multiple ways to solve the same problem.

If you’re just getting started, pick a simpler tool and focus on getting something working.

In my case, I eventually moved to Google’s Agent Development Kit (ADK). It’s easy to use, supports multi-agent flows, tool integration, memory, and works great with Google’s infrastructure like MCP and A2A.

I’ve used it since the early versions and reported a few bugs — the team behind it is active and responsive, and the framework has improved fast.

Final Thoughts

Building AI agents is hard. It’s not magic. It’s not plug-and-play. But it is possible — and incredibly satisfying when it works.

The biggest unlock isn’t the model. It’s how you design your prompts, your tools, your UX, and your system architecture to work with the model — not against it.

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I’d love to hear your perspective:

What lessons have you learned while building or exploring AI agents? What’s worked — and what hasn’t?