Recently, AI agents have often been described as systems that can “take over everything.”

They can write code, run projects, and execute complex workflows automatically. Some people even believe they can already replace engineers.

After using them in practice, however, I have reached almost the opposite conclusion:

Agents are powerful, but their boundaries are just as clear.

1. A Context Window Is Not Memory

People often point out that:

“Models now support 200k-token context windows, or even more.”

But this reflects a misunderstanding.

A context window is only the range of information visible during the current inference. It is not memory.

Once information falls outside that window, it is either:

  • discarded,
  • compressed, or
  • retrieved and spliced back in.

Whichever method is used, the underlying operation is the same:

Reconstruction, not possession.

2. An Interruption Does Not Correct the System; It Reconstructs It

While working with agents, I noticed a crucial problem:

👉 They cannot be corrected reliably in the middle of execution.

When you realize that an agent is heading in the wrong direction, your choices are limited:

  • let it continue, or
  • interrupt it and provide more information.

The problem is that:

After the interruption, it interprets the entire context again.

That means:

  • new information is introduced,
  • semantic weights are redistributed, and
  • parts that were previously correct may be reinterpreted as well.

The result is that:

You are not correcting a system. You are rebuilding one.

3. The Longer the Workflow, the Greater the Drift

Agents perform well on short tasks, but a clear phenomenon emerges in longer workflows:

Semantic drift

It often looks like this:

  • the early steps are correct,
  • small deviations appear in the middle,
  • later steps move farther from the original goal, and
  • the process eventually enters a loop or produces useless work.

If you let an agent run continuously for hours or longer, you may discover that:

It consumes a vast number of tokens without converging on a result.

4. The Fundamental Problem: Structure Cannot Persist

The root cause is not that the model is insufficiently intelligent. It is that:

Structure cannot be carried forward reliably.

What we currently call agent “memory” is essentially:

  • retrieval,
  • assembly, and
  • temporary context.

It is not:

  • a set of invariants,
  • a constraint system, or
  • an evolving structure.

5. Why Does This Matter?

Because the essence of a complex system is not simply to complete tasks. It is:

To preserve structural stability while change is taking place.

A system can tolerate local errors, but it cannot afford to lose structural consistency.

6. Conclusion

A more realistic assessment, then, is:

  • agents are well suited to tasks,
  • but not yet to systems.

Unless AI can one day make:

Structure itself persistent, rather than depending on humans to maintain it

the conclusion remains:

The structural sovereignty of engineering is still in human hands.