I've noticed something interesting about AI agent development.

Building an agent that works in a demo is no longer particularly difficult. Connect an LLM to a few tools, give it a task, add some memory, and you can get something impressive running surprisingly quickly.

But take that same agent into a real environment, and things become different.

Sometimes it calls the wrong tool. Sometimes it repeats an action. Sometimes it gets stuck trying to solve a problem that should have been simple.

And that's where the real engineering begins.

The problem isn't always the model. Quite often, it's the architecture around it.

Autonomy isn't the same as reliability

One mistake I see in agent design is giving the model too much control.

We expect an agent to understand a request, make decisions, execute tools, handle errors, and verify its own results.

But should an LLM really control every part of that process?

Consider a simple example: an AI agent handling customer refunds.

The model can understand the complaint and recommend whether a refund is appropriate. But actually processing the refund should involve business rules, permission checks, and transaction validation.

I prefer separating these responsibilities.

Let the model reason. Let deterministic application logic enforce the rules.

This makes the system easier to debug and significantly reduces the risk of unexpected actions.

Tool calling creates problems we often underestimate

Connecting APIs to an agent looks simple until something goes wrong.

Imagine an agent sending a payment request. The external API processes it, but the response times out.

The agent doesn't know whether the operation succeeded.

If it blindly retries, the same operation might happen twice.

This isn't an LLM problem. It's a distributed-systems problem.

We need proper timeouts, bounded retries, idempotency keys, structured error responses, and execution tracking.

AI agents don't eliminate traditional software engineering challenges. They introduce another layer of complexity on top of them.

More memory doesn't mean better intelligence

Another interesting challenge is memory.

Developers often assume that giving an agent more historical context will make it smarter.

In reality, irrelevant or outdated information can make its decisions worse.

For example, a coding agent working with a large repository needs relevant files, dependencies, and current implementation details. Retrieving everything isn't useful.

The important question isn't how much information an agent remembers.

It's whether it retrieves the right information at the right time.

Memory needs relevance, freshness, and proper context management.

The problem nobody should ignore: evaluation

An agent completing a task doesn't automatically mean it completed the task correctly.

Imagine a workflow that successfully generates a report but uses outdated information or exposes data it shouldn't access.

A simple success message would hide the actual problem.

We need to evaluate more than the final response.

I would look at task correctness, tool execution, unnecessary retries, latency, token consumption, and safety violations.

Most importantly, evaluation should include an independent way to verify the result rather than trusting the same model that performed the task.

What I would change before deploying an agent

Before calling an agent production-ready, I'd ask a few uncomfortable questions:

  • What happens when an API fails?
  • Can the agent repeat an irreversible action?
  • Who validates its decisions?
  • Can we reconstruct its execution history?
  • What happens when retrieved context is incorrect?
  • Does it know when to stop and ask for human intervention?

These questions might not make an impressive demo, but they matter when real users and business operations depend on the system.

My takeaway

I don't think the biggest challenge in AI agent engineering is making agents more autonomous.

It's learning where autonomy should stop.

A reliable agent needs more than a capable model. It needs controlled execution, good context management, independent evaluation, and clear boundaries.

We're getting better at building agents that can do things.

Now we need to get equally good at building agents that do the right things, for the right reasons, without creating problems along the way.

What has been your biggest challenge when moving an AI agent from a working prototype to a real production environment?