Why are AI agents unreliable?

Agents are unreliable because they are non-deterministic, they act through fallible tools and services, and small errors compound across every step of a task.

Non-determinism

The same prompt can produce different behavior on different runs. That variance is useful for creativity and poison for reliability, because it means correctness is probabilistic. You are not asking "does this work," you are asking "how often does this work."

Errors compound across steps

A single step that is 95 percent reliable sounds fine. Chain ten of them and the whole task is only about 60 percent reliable, because the failures multiply. Most real agents are long chains, so small per-step error rates become large end-to-end failure rates. This is why multi-step workflows are where reliability goes to die.

The world pushes back

Agents act through tools and external services that rate limit, time out, return stale data, and reject writes. An agent that assumed every call succeeds will break the moment the real world disagrees. Much of reliability engineering is simply refusing to assume the happy path.