為何自主式AI代理在實際生產環境中會失敗

They plan tasks, call tools, self-correct, and complete workflows end-to-end.
But when teams try to deploy them in production — especially where money, safety, or compliance is involved — the same problems appear again and again.

Not because the models are inaccurate, but because the system is structurally unsafe.

Here are the failure modes I keep seeing:

Non-replayable decisions
Agent behavior depends on implicit context, dynamic reasoning, and probabilistic paths.
When something goes wrong, you can’t reliably replay why a decision was made.

Probabilistic components with execution authority
Language models generate plausible outputs, not deterministic decisions.
Giving them final execution power creates an unbounded risk surface.

No hard veto layer
Many agent systems “try another tool” or “fill in missing intent” instead of failing closed.
That’s resilience in demos, but risk amplification in real systems.

Ambiguous responsibility
When an agent acts autonomously, it becomes unclear who actually approved the action.
In regulated or high-consequence domains, this alone blocks deployment.

The core issue isn’t intelligence — it’s accountability.

In production systems, AI can be extremely valuable as:

a semantic interpreter

a risk signal generator

a decision-support component

But final decisions must remain:

deterministic

replayable

auditable

vetoable by humans

Until agent architectures treat controllability as a first-class requirement, autonomy will remain a demo feature — not a production one.